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One of the central insights of artificial intelligence is that expert performance requires domain-specific knowledge, and work on knowledge engineering has led to many AI systems that are now regularly used in industry and elsewhere. The ultimate test of machine learning, the subfield of Al that studies the automated acquisition of knowledge, is the application of its techniques to produce similar results. Recent successes in real-world applications of machine learning suggest the time was ripe for a meeting on this topic. For this reason, Pat Langley (Siemens Corporate Research) and Yves Kodratoff (Universite de Paris, Sud) organized an invited workshop on applications of machine learning. The goal of the gathering was to familiarize participants with existing applications of computational learning methods and to explore the potential for additional ones in the private and public sector. To this end, it emphasized fielded applications that are in actual use, and it downplayed differences among the specific learning methods employed, focusing instead on the machinations necessary to obtain successful results in real-world domains. The meeting took place at the University of Massachusetts, Amherst, on June 30 and July 1, 1993, immediately following the Tenth International Conference on Machine Learning. Approximately 30 participants listened to 12 invited presentations, most of which dealt with specific applications of machine learning. The attendees also took part in lively discussions about the issues that arise in developing fielded applications, the relation of such work to the rest of machine learning, and the potential for future applications.

This project reviews the use of machine learning (ML) models in psychological research over the past decade. Alongside examining transparency standards, it investigates how authors address both the technical aspects and the conceptual or interpretative dimensions of ML applications. The review aims to provide a foundation for outlining current modeling and reporting practices in psychological research and for identifying areas in need of improvement. Detailed rationale, objectives, and outcomes are provided in the annotated document.

This project reviews the use of machine learning (ML) models in psychological research over the past decade. Alongside examining transparency standards, it investigates how authors address both the technical aspects and the conceptual or interpretative dimensions of ML applications. The review aims to provide a foundation for outlining current modeling and reporting practices in psychological research and for identifying areas in need of improvement. Detailed rationale, objectives, and outcomes are provided in the annotated document.

In recent years, Machine Learning (ML) techniques have been extensively deployed for computer vision tasks, particularly visual classification problems, where new algorithms reported to achieve or even surpass the human performance... By: Dou Goodman, Yang Wang & Hao Xin Full Abstract & Presentation Materials: https://www.blackhat.com/asia-20/briefings/schedule/#attacking-and-defending-machine-learning-applications-of-public-cloud-18725 Source: https://www.youtube.com/watch?v=CZaEu8RBcFw Uploader: Black Hat

Scoping review with the objective of analyzing the regulatory framework for applications of Artificial Intelligence and Machine Learning in medicine in the member countries of Mercosur (Brazil, Argentina, Paraguay and Uruguay), identifying their main characteristics, convergences, differences and gaps.

Communication Service Providers (CSPs) are in a unique position to utilize their vast transactional data assets generated from interactions of subscribers with network elements as well as with other subscribers. CSPs could leverage its data assets for a gamut of applications such as service personalization, predictive offer management, loyalty management, revenue forecasting, network capacity planning, product bundle optimization and churn management to gain significant competitive advantage. However, due to the sheer data volume, variety, velocity and veracity of mobile subscriber datasets, sophisticated data analytics techniques and frameworks are necessary to derive actionable insights in a useable timeframe. In this paper, we describe our journey from a relational database management system (RDBMS) based campaign management solution which allowed data scientists and marketers to use hand-written rules for service personalization and targeted promotions to a distributed Big Data Analytics platform, capable of performing large scale machine learning and data mining to deliver real time service personalization, predictive modelling and product optimization. Our work involves a careful blend of technology, processes and best practices, which facilitate man-machine collaboration and continuous experimentation to derive measurable economic value from data. Our platform has a reach of more than 500 million mobile subscribers worldwide, delivering over 1 billion personalized recommendations annually, processing a total data volume of 64 Petabytes, corresponding to 8.5 trillion events.

By constructing the Fisher Information Matrix (FIM) and its inverse (IFIM), the current study provides an info-geometric characterization of the transient M/M/1 queue manifold. Furthermore, stability's effect on IFIM's existence and its investigation of the  Geodesic Equations (GEs)  of motion have been made clear. Potentially even more, the article highlights several info-geometric machine learning applications. This in essence would manifest the huge contributions provided by this paper, which would in turn open new plethora of unlimited opportunities to the exploration of new trends in machine learning. In closing, some difficult open problems are discussed along with the next stage of the research.

This report summaries the talks presented at the Workshop on Fielded Applications of Machine Learning, and draws some initial conclusions about the state of machine learning and its potential for solving real-world problems.

The accuracy of machine learning systems is a widely studied research topic. Established techniques such as cross-validation predict the accuracy on unseen data of the classifier produced by applying a given learning method to a given training data set. However, they do not predict whether incurring the cost of obtaining more data and undergoing further training will lead to higher accuracy. In this paper we investigate techniques for making such early predictions. We note that when a machine learning algorithm is presented with a training set the classifier produced, and hence its error, will depend on the characteristics of the algorithm, on training set's size, and also on its specific composition. In particular we hypothesise that if a number of classifiers are produced, and their observed error is decomposed into bias and variance terms, then although these components may behave differently, their behaviour may be predictable. We test our hypothesis by building models that, given a measurement taken from the classifier created from a limited number of samples, predict the values that would be measured from the classifier produced when the full data set is presented. We create separate models for bias, variance and total error. Our models are built from the results of applying ten different machine learning algorithms to a range of data sets, and tested with "unseen" algorithms and datasets. We analyse the results for various numbers of initial training samples, and total dataset sizes. Results show that our predictions are very highly correlated with the values observed after undertaking the extra training. Finally we consider the more complex case where an ensemble of heterogeneous classifiers is trained, and show how we can accurately estimate an upper bound on the accuracy achievable after further training.

Assessing the feasibility and acceptability of the AI and machine learning in this area can help to understand the opportunities and possible barriers for the implementation and further provide evidence to support the real-world practice. To our knowledge, no study has reviewed and synthesized the current evidence on this topic. Therefore, this scoping review aims to systematically locate and review literature on the feasibility and acceptability of AI and machine learning applications in health care for anxiety-related disorders. This will enable the identification of research gaps in this topic and then contribute to improving future research and supporting evidence-based real-world practice of AI and machine learning in mental health care.

The goal of the research described here is to develop simulation programs that can be used for formative evaluation during the instructional design process. Such simulations are called pseudo-students, because they simulate human students learning from the given instruction. However, unlike human students, pseudo-students keep a detailed trace of the learning so that the designer can discover the causes of undesirable pedagogical outcomes. For instance, one pseudo-student, Sierra, helped demonstrate that many systematic arithmetic errors are caused by incomplete and poorly sequenced instruction Mind bugs: The origins of procedural misconceptions. Most of these design defects would be easy to fix now that they have been detected. We describe Sierra and a second pseudo-student, Cascade, which is being developed for modeling the learning of college physics. Keywords: Cognitive modelling; Learning; Formative evaluation.

Data science and machine learning have become transformative tools for addressing challenges across diverse domains. This paper presents three projects that leverage these technologies to deliver innovative solutions in healthcare, entertainment, and personal finance.  The first project focuses on heart disease prediction, utilizing machine learning algorithms to analyze key medical parameters, provide early diagnostic insights. By enabling timely interventions, this approach has the potential to significantly improve patient outcomes and alleviate the burden on healthcare systems. The second project delves into viewer analytics for the OTT platform Hotstar. Using advanced data analysis techniques, the project identifies patterns in viewer behavior, including preferences, peak viewing times, and genre popularity. These insights can be instrumental in optimizing content recommendations, enhancing user engagement, and driving business growth in the entertainment industry. The third project introduces a smart expense tracker designed to empower individuals in managing their finances. By employing predictive analytics, the tracker not only categorizes expenses but also forecasts future spending patterns, offering personalized budgeting advice and promoting financial well-being. Collectively, these projects demonstrate the versatility and impact of machine learning and data analytics in addressing real-world problems. By applying cutting-edge methodologies to distinct sectors, the work underscores the far-reaching potential of data-driven innovation in shaping a smarter, more efficient future.

With the increased use of the internet, web applications and websites are becoming more and more common. With the increased use, cyber-attacks on web applications and websites are also increasing. Of all the different types of cyber-attacks on web applications and websites, XSS (Cross-Site Scripting) attacks are one of the most common forms of attack. XSS attacks are a major problem in web security and ranked as number two web application security risks in the OWASP (Open Web Application Security Project) Top 10. Traditional methods of defence against XSS attacks include hardware and software-based web application firewalls, most of which are rule and signature-based. Rule-based and signature-based web application firewalls can be bypassed by obfuscating the attack payloads. As such, rule-based and signaturebased web application firewalls are not effective against detecting XSS attacks for payloads designed to bypass web application firewalls. This paper aims to use machine learning to detect XSS attacks using various ML (machine learning) algorithms and to compare the performance of the algorithms in detecting XSS attacks in web applications and websites

Artificial intelligence (AI) has been developing rapidly in recent years in terms of software algorithms, hardware implementation, and applications in a vast number of areas. In this review, we summarize the latest developments of applications of AI in biomedicine, including disease diagnostics, living assistance, biomedical information processing, and biomedical research. Various automated systems and tools like Braincomputer interfaces (BCIs), arterial spin labelling (ASL) imaging, ASL-MRI, biomarkers, Natural language processing (NLP) and various algorithms helps to minimize errors and control disease progression. The computer assisted diagnosis, decision support systems, expert systems and implementation of software may assist physicians to minimize the intra and inter-observer variability. In this paper, a detailed literature review on application and implementation of Machine Learning, Deep Learning and Artificial Intelligence in the healthcare industry by various researchers.

In contemporary computer sciences, machine learning is one of the areas. A lot of research has been carried out to make machines intelligent. Learning is an important feature of computers as well as normal human behavior. Different approaches have been developed in several fields of operation for the same. Conventional machine learning algorithms have been introduced. Researchers have worked hard to develop the exactness of these learning algorithms. They have thought of another level contributing to a broad definition of learning. Deep study is a machine learning subset. Few deep learning implementations have been researched until now. This would undoubtedly resolve concerns in many new areas of application, sub-domains that use profound learning. This paper illustrates a study of historical and future areas, subdomains and implementations for computer learning.

Amity Journal of Computational Sciences (AJCS) Volume 2 Issue 2 ISSN: 2456-6616 (Online)

The integration of electric vehicles (EVs) into mainstream transportation systems is contingent upon the development of efficient and convenient charging technologies. Wireless charging, in particular, presents a promising solution to address the limitations of traditional plug-in charging methods. However, optimizing wireless charging techniques for EVs remains a complex challenge, with factors such as efficiency, alignment, and safety needing careful consideration. This paper explores the potential of leveraging machine learning (ML) algorithms to enhance the performance of wireless charging systems for EVs. By employing ML techniques, such as neural networks and genetic algorithms, in conjunction with real-time data analysis, the aim is to develop adaptive and intelligent charging systems capable of optimizing various parameters to improve efficiency, reliability, and userexperience. This research paper discusses the current state of wireless charging technologies, explores the application of machine learning in optimizing these systems, and presents potential avenues for future research and development

The objective of this review is to analyze the studies published to date on PK/PD mod-eling for antibiotic dose optimization using AI techniques. In addition, this review aims to evaluate in detail the different AI techniques used and the statistical metrics applied to assess the accuracy of the prediction and its comparison with the conven-tional monitoring techniques.

Perma.cc archive of https://www.drugpatentwatch.com/blog/8-applications-machine-learning-pharmaceutical-industry/ created on 2022-09-06 20:41:13.355383+00:00.

A scoping review which covers the different types or systems of Artificial intelligence and machine learning technologies and algorithms and its applications in various domains of Orthognathic surgery

Title: Applications of Artificial Intelligence and Machine Learning to Office Laryngoscopy: A Scoping Review Authors: Peter Yao Alexander German Katerina Andreadis Anaïs Rameau Contact: Peter Yao [email protected] Weill Cornell Medical College Review Questions: How has machine learning, deep learning, and artificial intelligence been applied to office laryngoscopy imaging? Searches: We searched the databases of MEDLINE, EMBASE, COCHRANE, Web of Science, and IEEE Explore. We searched using a combination of database-specific subject headings and text words for the main concepts of artificial intelligence and laryngoscopy, laryngeal structures or laryngeal pathology. The search strategy was customized for each database. Types of study to be included: There are no restrictions on the methodology, the type of model developed, or publication date. Studies not written in English, were not peer reviewed, or were review papers were excluded. Reviews were excluded from the analysis, but we screened the reference lists of relevant reviews to identify potential eligible studies. Condition or domain being studied: Artificial intelligence models applied to laryngoscopic videos or images. Participants/population: Patients undergoing office laryngoscopies. Intervention: Machine learning and artificial intelligence algorithms applied to video laryngoscopy or laryngoscopy images. Comparator/control: Not applicable. Primary Outcome: Identify, appraise, and synthesize the applications of artificial intelligence to laryngoscopy including by not limited to identification, detection, and segmentation of pathology, informative frame filtering, and anatomical segmentation. Additional outcome: This review will also identify existing gaps in the literature, and help formulate the best approach for applying artificial intelligence to laryngoscopic imaging to guide existing and future researchers. Data extraction: Screening: Literature search results were imported to Covidence, an Internet-based systematic review data management software that facilitates collaboration among reviewers during the study selection process. Articles were screened using a two-step process. In the first step, articles were screened by title and abstract by teams of two reviewers working independently. In the second step, articles that passed the title-abstract screen were screened by full-text by teams of two reviewers working independently. A third reviewer reviewed articles and resolved disagreements through consensus when the original two reviewers were not able to reach one. We will calculate the adjusted kappa statistic to measure interrater agreement for eligibility screening. Data Collection We divided the items within the data collection form into four blocks: (1) study information including publication year, author information, funding or sponsorship information, type of study, journal name and PICO elements; (2) database information including data source and sample size; (3) patient demographic information including gender, age, race, and disease diagnosis; (4) ML methodological information including ML model name, type, task, data classes, class split, how ground-truth labels are determined, objective function, and model performance. Data Synthesis: A systematic narrative synthesis will be provided with information presented in the text and tables to summarize and explain the characteristics and findings of the included studies. Analysis of subgroups or subsets: We will analyze studies in groups based on their objectives. For example, studies focused on diagnosis will be analyzed separately from studies focused on anatomical segmentation.

Appendicitis is one of the most common surgically treated diseases, with more than 300,000 appendectomies being performed in the United States annually. Despite its common nature, appendicitis can be difficult to diagnose at times even with the established criteria of systems like the Alvarado Score. Atypical presentations and poor predictive value of laboratory tests complicate diagnoses and decisions for surgical intervention. CT imaging improves sensitivity and specificity of diagnoses, however this tool bears the drawbacks of high operator dependency and needless radiation exposure. The need for a framework that can inform selective use of CT scans, especially for equivocally-scored cases of appendicitis, warrants the use of machine learning. Machine learning is a rapidly evolving field with increasing applications in healthcare at-large. This artificial intelligence approach uses historical data to train a model that captures existing patterns in data to predict outcomes. The aim of this review is to classify these novel uses of various machine learning algorithms in the context of appendicitis management and examine their potential to reshape pre-operative and post-operative decision-making.

Farming is not easy to approach things, as we must feed approximately 8 million people daily. Traditional farming is an old-fashioned technique that takes so much time to produce. Due to this, we need some modern technology to increase our production. But research has shown that in 50 years, there will be 8.3 billion more people on Earth than there are now. It will need an extra 109 hectares of cropland, which doesn't already exist, to feed these new immigrants. There are also clear signs that pollution and the rise in population are worsening. Despite the benefits of smart traditional farming, there are a number of drawbacks to traditional farming. Regardless of soil pollution, water pollution, land pollution, energy loss, climatic conditions, electricity waste, and transportation costs, another option will be better for the food production industry. According to the research, to feed 8 billion people, we would need an additional 109 hectares of land. That is the typical problem that the farmers will face because most of the hollandaise nowadays. So, using this building concept, we will implement indoor farming, which means planting fruits and vegetables inside the tall building, an advanced form of a greenhouse. The innovative idea, also known as Vertical Farming (VF), integrates agricultural design with building design in a tall structure inside of cities. Implementing VF will solve many challenges by utilizing modern machine learning, IoT, and AI techniques, increasing VF productivity and quality. In VF, aeroponics consumes 98% less water than conventional farming. Human health, comfort, and productivity directly correlate with the indoor climate. Systems for vertical plant walls with sensors and actuators have become a good way to control the environment inside a building. They are using a set-up of vertical plant walls with anomaly detection techniques based on machine learning to increase automation and intelligence for predictive indoor climate maintenance.

Post-genomic research deals with challenging problems in screening genomes of organisms for particular functions or potential for being the targets of genetic engineering for desirable biological features. 'Phenotyping' of wild type and mutants is a time-consuming and costly effort by many individuals. This article is a preliminary progress report in research on large-scale automation of phenotyping steps (imaging, informatics and data analysis) needed to study plant gene-proteins networks that influence growth and development of plants. Our results undermine the significance of phenotypic traits that are implicit in patterns of dynamics in plant root response to sudden changes of its environmental conditions, such as sudden re-orientation of the root tip against the gravity vector. Including dynamic features besides the common morphological ones has paid off in design of robust and accurate machine learning methods to automate a typical phenotyping scenario, i.e. to distinguish the wild type from the mutants.

This systematic review aims to evaluate the application of machine learning (ML) and artificial intelligence (AI) methods in analyzing data from wearable inertial measurement units (IMUs) in clinical, biomechanical, and healthcare settings. Specifically, we seek to identify: 1) the types of ML/AI models used, 2) the motions and sensor-derived features analyzed, 3) the applications and outcomes assessed, and 4) the performance metrics reported. We aim to highlight the most promising analytical strategies and guide future research toward the development of objective sensor-based functional assessment tools for clinical use. First, we ask: How are machine learning and artificial intelligence methods applied to assess human movement impairments associated with musculoskeletal and neurological conditions using wearable inertial measurement unit (IMU) data in clinical, biomechanical, or healthcare applications? Secondly, we also ask: How do machine learning and artificial intelligence methods complement or extend traditional biomechanical and statistical analysis methods in assessing human movement?

The prevalence of machine learning (ML) applications in the gambling field have been fueled by an accumulation of customer data and computing power. Industry operators, for example, deploy ML to support a variety of use-cases including recommendation systems, fraud detection, customer churn, and customer relationship marketing. Additionally, regulators and government bodies endorse the use of ML for harm minimization and prevention. Several gambling consumer protection products are powered by ML (e.g., BetBuddy, Neccton, Future Anthem, Mindway AI, etc.) and ML methods have also garnered interest amongst the academic community (for a review, see Ghaharian et al., 2022). While ML provides many benefits, its adoption also presents risk and broader ethical concerns. These may include, for example, bias/fairness in predictions, broken data pipelines, and inappropriate training data and/or methods. It is important that these issues are identified and assessed to guide the future development, use, and governance of ML in gambling. Accordingly, this study seeks to find out: What is known about the potential risks and ethical concerns of machine learning applications in gambling? We will conduct a scoping literature review to address this research question. This review type is best suited to answer broad questions and to collect the breadth of literature on a topic. It also allows flexibility to include a range of articles (including grey literature). We have devised this protocol in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (i.e., the PRISMA-ScR in Tricco et al., 2018). The attached file contains completed PRISMA-ScR checklist items.

The prevalence of machine learning (ML) applications in the gambling field have been fueled by an accumulation of customer data and computing power. Industry operators, for example, deploy ML to support a variety of use-cases including recommendation systems, fraud detection, customer churn, and customer relationship marketing. Additionally, regulators and government bodies endorse the use of ML for harm minimization and prevention. Several gambling consumer protection products are powered by ML (e.g., BetBuddy, Neccton, Future Anthem, Mindway AI, etc.) and ML methods have also garnered interest amongst the academic community (for a review, see Ghaharian et al., 2022). While ML provides many benefits, its adoption also presents risk and broader ethical concerns. These may include, for example, bias/fairness in predictions, broken data pipelines, and inappropriate training data and/or methods. It is important that these issues are identified and assessed to guide the future development, use, and governance of ML in gambling. Accordingly, this study seeks to find out: What is known about the potential risks and ethical concerns of machine learning applications in gambling? We will conduct a scoping literature review to address this research question. This review type is best suited to answer broad questions and to collect the breadth of literature on a topic. It also allows flexibility to include a range of articles (including grey literature). We have devised this protocol in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (i.e., the PRISMA-ScR in Tricco et al., 2018). The attached file contains completed PRISMA-ScR checklist items.

A scoping review that aims to answer the question: How has machine learning, deep learning, and artificial intelligence been applied to office laryngoscopy imaging?

Crowdfunding platforms are important for startups since they offer diverse financing options, market validation, and promotional opportunities through an investor community. These platforms provide detailed company in-formation, aiding informed investment decisions within a regulated and secure environment. Machine learning (ML) techniques are vital in analyzing large data sets, detecting anomalies and fraud, and enhancing deci-sion-making and business strategies. A systematic review employed PRISMA guidelines, which studied how ML improves fraud detection on digital crowdfunding platforms. The analysis includes English-language studies from peer-reviewed journals published between 2018 and 2023 to analyze the pre- and post-COVID-19 pandem-ic. The findings indicate that ML techniques such as Random Forest, Support Vector Machine, and Artificial Neural Networks significantly enhance the predictive accuracy and utility of tax planning for startups consider-ing equity crowdfunding. The United States, Germany, Canada, Italy, and Turkey do not present statistically sig-nificant differences at the 95% confidence level, standing out for their notable academic visibility. Florida Atlan-tic and Cornell Universities, Springer and John Wiley & Sons Ltd publishing houses, and the Journal of Business Ethics and Management Science magazines present the highest citations without statistical differences at the 95% confidence level.

Appendicitis is one of the most common surgically treated diseases, with more than 300,000 appendectomies being performed in the United States annually. Despite its common nature, appendicitis can be difficult to diagnose at times even with the established criteria of systems like the Alvarado Score. Atypical presentations and poor predictive value of laboratory tests complicate diagnoses and decisions for surgical intervention. CT imaging improves sensitivity and specificity of diagnoses, however this tool bears the drawbacks of high operator dependency and needless radiation exposure. The need for a framework that can inform the selective use of CT scans, especially for equivocally-scored cases of appendicitis, warrants the use of machine learning. Machine learning is a rapidly evolving field with increasing applications in healthcare at-large. This artificial intelligence approach uses historical data to train a model that captures existing patterns in data to predict outcomes. The aim of this review is to classify these novel uses of various machine learning algorithms in the context of appendicitis management and examine their potential to reshape pre-operative and post-operative decision-making.

An important area of application for machine learning is in automating the acquisition of knowledge bases required for expert systems. In this paper, we review the major paradigms for machine learning, including neural networks, instance-based methods, genetic learning, rule induction, and analytic approaches. We consider rule induction in greater detail and review some of its recent applications, in each case stating the problem, how rule induction was used, and the status of the resulting expert system. In closing, we identify the main stages in fielding an applied learning system and draw some lessons from successful applications.

The goal of the research described here is to develop simulation programs that can be used for formative evaluation during the instructional design process. Such simulations are called pseudo-students because they simulate human students learning from the given instruction. However, unlike human students, pseudo-students keep a detailed trace of the learning so that the designer can discover the causes of undesirable pedagogical outcomes. For instance, one pseudo-student, psuedo-student(Sierra), helped demonstrate that many systematic arithmetic errors are caused by incomplete and poorly sequenced instruction (VanLehn, K. (1990) psuedo-students (Mind bugs: The origins of procedural misconceptions), Cambridge, MA: MIT Press). Most of these design defects would be easy to fix now that have been detected. We describe Sierra and a second pseudo-student, Cascade, which is being developed for modeling the learning of college physics.

Perma.cc archive of https://royalsocietypublishing.org/doi/full/10.1098/rsta.2017.0363 created on 2022-08-28 22:40:26.012821+00:00.

This study will examine the existing research on the use of machine learning (ML) in the education. It will identify the various ways ML has been applied to enhance educational processes. The review will also discusse the challenges faced in integrating ML into educational settings. Additionally, it outlines potential future research directions. This scoping review aims to provide a comprehensive overview for educators, technologists, and policymakers interested in the applications of machine learning in education.

Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age, characterized by hormonal imbalances, irregular menstruation, and polycystic ovaries. Early and accurate prediction of PCOS is vital for timely intervention and management. Machine learning (ML) algorithms have emerged as powerful tools for predicting PCOS by analyzing complex datasets efficiently. This review focuses on studies from 2014 to 2024 that apply ML techniques to predict PCOS, utilizing databases such as PubMed, Scopus, and Google Scholar. Supervised learning algorithms like Random Forests and Support Vector Machines, deep learning models, and hybrid approaches are commonly employed. These models leverage data such as clinical symptoms, biochemical markers, and ultrasound imaging results to enhance prediction accuracy. Despite promising results, challenges persist, including data imbalance, feature selection, and model interpretability. Opportunities for improvement include integrating multi-omics data, advancing personalized medicine, and developing accessible cloud-based tools. This review highlights the performance metrics of various ML algorithms, underscoring their potential to revolutionize PCOS diagnosis. By integrating ML models into clinical practice, healthcare providers can improve diagnostic efficiency, reduce costs, and offer tailored care to patients, paving the way for more effective PCOS management.

This project is a scoping review evaluating for the presence of bias on any of the PROGRESS-Plus criteria within medical applications of machine learning.

This systematic review examines the state of artificial intelligence and machine learning techniques applied to pediatric surgical pathology cases. Our results are expected to highlight promising avenues of artificial intelligence applications to pediatric pathology, as well as showcasing current gaps in performance and clinical workflow implementation.

This systematic review examines the state of artificial intelligence and machine learning techniques applied to pediatric surgical pathology cases. Our results are expected to highlight promising avenues of artificial intelligence applications to pediatric pathology, as well as showcasing current gaps in performance and clinical workflow implementation.

Collectively, lung cancer, breast cancer and melanoma was diagnosed in over 535,340 people out of which, 209,400 deaths were reported [13]. It is estimated that over 600,000 people will be diagnosed with these forms of cancer in 2015. Most of the deaths from lung cancer, breast cancer and melanoma result due to late detection. All of these cancers, if detected early, are 100% curable. In this study, we develop and evaluate algorithms to diagnose Breast cancer, Melanoma, and Lung cancer. In the first part of the study, we employed a normalised Gradient Descent and an Artificial Neural Network to diagnose breast cancer with an overall accuracy of 91% and 95% respectively. In the second part of the study, an artificial neural network coupled with image processing and analysis algorithms was employed to achieve an overall accuracy of 93% A naive mobile based application that allowed people to take diagnostic tests on their phones was developed. Finally, a Support Vector Machine algorithm incorporating image processing and image analysis algorithms was developed to diagnose lung cancer with an accuracy of 94%. All of the aforementioned systems had very low false positive and false negative rates. We are developing an online network that incorporates all of these systems and allows people to collaborate globally.

In this digital world, the data is wealth, this data is analyzed, developed and applied to specific applications using some well-developed algorithms known as Machine learning (ML). Machine learning algorithms are supervised, unsupervised, semi-supervised andreinforcement types. Deep learning (DL) is also a method of analyzing the data on a large scale. Deep learning is a further subdivision of The subsection of ML is deep learning and measures a particular type of learning that involves the use of artificialneural networks (ANN).This paper provides group of different machine learning terminologies for quick reference. This study is important to focus on different machine learning techniques and their connection in various real-world applications such as smart cities, cyber security, healthcare, agriculture and intelligent transportation systems. In this paper, machine learning concepts, different types of architectures, the challenges, various real world applications are discussed.

Checking software application suitability using automated software tools has become a vital element for most organisations irrespective of whether they produce in-house software or simply customise off-the-shelf software applications for internal use. As software solutions become ever more complex, the industry becomes increasingly dependent on software automation tools, yet the brittle nature of the available software automation tools limits their effectiveness. Companies invest significantly in obtaining and implementing automation software but most of the tools fail to deliver when the cost of maintaining an effective automation test suite exceeds the cost and time that would have otherwise been spent on manual testing. A failing in the current generation of software automation tools is they do not adapt to unexpected modifications and obstructions without frequent (and time expensive) manual interference. Such issues are commonly acknowledged amongst industry practitioners, yet none of the current generation of tools have leveraged the advances in machine learning and artificial intelligence to address these problems. This paper proposes a framework solution that utilises machine learning concepts, namely fuzzy matching and error recovery. The suggested solution applies adaptive techniques to recover from unexpected obstructions that would otherwise have prevented the script from proceeding. Recovery details are presented to the user in a report which can be analysed to determine if the recovery procedure was acceptable and the framework will adapt future runs based on the decisions of the user. Using this framework, a practitioner can run the automated suits without human intervention while minimising the risk of schedule delays.

Educational institutions, propelled by digital transformation and sophisticated machine learning (ML) algorithms, amass plentiful data, facilitating the execution of complicated decision-making tasks previously inconceivable. ML’s pervasive influence extends beyond pedagogy and research, profoundly altering the fabric of academia and reshaping university functionalities. Its deployment in university administration enhances efficacy, efficiency, and operational streamlining across diverse levels. This article conducts a comprehensive review of extant knowledge pertaining to the diverse applications of ML in non-teaching domains within academic settings, delineating avenues for future research. The recognized findings furnish a robust foundation for the further exploration and refinement of ML applications, particularly within the administrative and operational realms of academia. A consequential outcome of this transformative integration is the mitigation of teachers’ administrative burdens. In practical terms, this liberation affords educators the opportunity to redirect their time and energy towards their primary responsibilities of educating and fostering the intellectual development of their students.

The objective of this work is to develop methodologies to detect, and report the non-compliant images with respect to indian space research organisation (ISRO) recruitment requirements. The recruitment software hosted at U. R. rao satellite centre (URSC) is responsible for handling recruitment activities of ISRO. Large number of online applications are received for each post advertised. In many cases, it is observed that the candidates are uploading either wrong or non-compliant images of the required documents. By non-compliant images, we mean images which do not have faces or there is not enough clarity in the faces present in the images uploaded. In this work, we attempt to address two specific problems namely: 1) To recognise image uploaded to recruitment portal contains a human face or not. This is addressed using a face detection algorithm. 2) To check whether images uploaded by two or more applications are same or not. This is achieved by using machine learning (ML) algorithms to generate similarity score between two images, and then identify the duplicate images. Screening of valid applications becomes very challenging as the verification of such images using a manual process is very time consuming and requires large human efforts. Hence, we propose novel ML techniques to determine duplicate and non-face images in the applications received by the recruitment portal.

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The stock market is considered the primary domain of importance in the financial sector where Artificial Intelligence combined with various algorithmic practices empowers investors with datadriven insights, enhancing decision-making, predicting trends, and optimizing risk management for more informed and strategic financial outcomes. This research paper delves into the real-world applications of machine learning and algorithmic trading, observing their historical evolution together and how both of these can go hand in hand to control risk and forecast the movement of a stock or an index and its future. The research is structured to provide comprehensive insights into two major subdomains in the application of AI in algorithmic trading: risk management in equity markets and predictive analysis of stock trends through the application of machine learning models and training the current existing data which is feasible and training them with respect to historical scenarios of various market trends along with various fundamental and technical analysis techniques with the help of various deep learning algorithms. For risk management of a portfolio in finance, various machine learning models can be employed, depending on the specific needs and goals of the portfolio manager or risk analyst and implementing various valueat-risk algorithms along with deep learning techniques in order to assess risk at particular trade position and to manage volatile trades at unprecedented situations. The significance of this research paper lies in its practical applicability, offering real-world solutions to enhance trading strategies and decision-making processes with a focus on mitigating risk and capitalizing on market opportunities and also giving clear insights with respect to the current practical limitations of application of the provided solution and future scope to overcome the same.

Recent major advances in the technology known as deep learning have reawakened global interest in machine learning and its potential to transform many technologies. In this report we review the different types of machine learning, outlining some of the places where it is currently used in Army research. Then we prognosticate where we see it applied to future Army research and operations. In doing so, we also identify gaps in current machine learning research that need to be filled for it to reach its full potential.

Ph D Thesis Peini