Aspects Of Machine Learning Research Articles

Analyzing sales proposal rejections via machine learning

The sales profession is fraught with customer rejection and defections. Understanding why customers say “no” to a sales proposal is complex given that factors at the organizational-, individual-, and interactional-level are synthesized in the customer’s decision-making process. Academics and practitioners alike therefore stand to benefit from greater understanding of this phenomenon. The current study leverages text-based machine learning on postmortem interview transcripts from 113 business-to-business sales failures, spanning over 1,500 pages of text, to provide exploratory insights into the reasons for sales proposal rejections. Results reveal several thematic facets of sales proposal failures from the perspective of the customer, along with insights that variance in topic salience—i.e., buyer focus on a few topics or a spread of dimensions—is contingent on supplier incumbency status. Specifically, using topic modeling, findings show that buyers converge on a distributed (concentrated) range of sales proposal rejection topics for in- (out-) supplier proposals. Additionally, the authors show how the text-based machine learning approach can highlight key areas of concern for firms, enabling them to effectively enact changes that will improve future outcomes. Collectively, this research contributes to efforts to bridge the chasm between theoretical, managerial, and technical aspects of machine learning in sales.

47 Plant Phenomics and Artificial Intelligence to Glean Information from Plant Sensing Technologies

Abstract One of the grand challenges of our generation is to get ready to feed 9 billion people by 2050 with sustainable use of environmental resources. However, our current agricultural system is not prepared for it. We are facing unprecedented challenges in adopting sustainable management practices, increasing production, and coping with pest and climate stressors that threaten yield; while running a profitable farm operation. In this talk, I will discuss our vision of a new cyber-plant-agricultural system that leads to an ultra-precision technology to monitor and manage plants or small plots at an individual level. To realize this grand vision, machine learning (ML) will play a large role. However, several key aspects of ML such as robustness, interpretability and data requirement need to be studied in the context of agriculture for successful deployment. In addition, high-throughput and effective plant phenotyping systems need to assimilate heterogeneous, multi-modal data for decision-making and should be suitable for distributed implementation for enhanced scalability. I will discuss a few success stories from our recent work to discuss these aspects of ML in plant agriculture that is relevant and potentially beneficial for the animal agriculture community.

Fundamental and practical aspects of machine learning for the peak picking of biomolecular NMR spectra

Rapid progress in machine learning offers new opportunities for the automated analysis of multidimensional NMR spectra ranging from protein NMR to metabolomics applications. Most recently, it has been demonstrated how deep neural networks (DNN) designed for spectral peak picking are capable of deconvoluting highly crowded NMR spectra rivaling the facilities of human experts. Superior DNN-based peak picking is one of a series of critical steps during NMR spectral processing, analysis, and interpretation where machine learning is expected to have a major impact. In this perspective, we lay out some of the unique strengths as well as challenges of machine learning approaches in this new era of automated NMR spectral analysis. Such a discussion seems timely and should help define common goals for the NMR community, the sharing of software tools, standardization of protocols, and calibrate expectations. It will also help prepare for an NMR future where machine learning and artificial intelligence tools will be common place.

Perspective of AI system for COVID-19 detection using chest images: a review.

Coronavirus Disease 2019 (COVID-19) is an evolving communicable disease caused due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) which has led to a global pandemic since December 2019. The virus has its origin from bat and is suspected to have transmitted to humans through zoonotic links. The disease shows dynamic symptoms, nature and reaction to the human body thereby challenging the world of medicine. Moreover, it has tremendous resemblance to viral pneumonia or Community Acquired Pneumonia (CAP). Reverse Transcription Polymerase Chain Reaction (RT-PCR) is performed for detection of COVID-19. Nevertheless, RT-PCR is not completely reliable and sometimes unavailable. Therefore, scientists and researchers have suggested analysis and examination of Computing Tomography (CT) scans and Chest X-Ray (CXR) images to identify the features of COVID-19 in patients having clinical manifestation of the disease, using expert systems deploying learning algorithms such as Machine Learning (ML) and Deep Learning (DL). The paper identifies and reviews various chest image features using the aforementioned imaging modalities for reliable and faster detection of COVID-19 than laboratory processes. The paper also reviews and compares the different aspects of ML and DL using chest images, for detection of COVID-19.

A Survey of Machine Learning Approaches Applied to Gene Expression Analysis for Cancer Prediction

Machine learning approaches are powerful techniques commonly employed for developing cancer prediction models using associated gene expression and mutation data. This manuscript provides a comprehensive review of recent cancer studies that have employed gene expression data from several cancer types (breast, lung, kidney, ovarian, liver, central nervous system and gallbladder) for survival prediction, tumor identification and stratification. We also provide an overview of biomarker studies that are associated with these cancer types. The survey captures multiple aspects of machine learning associated cancer studies, including cancer classification, cancer prediction, identification of biomarker genes, microarray, and RNA-Seq data. We discuss the technical issues with current cancer prediction models and the corresponding measurement tools for determining the activity levels of gene expression between cancerous tissues and noncancerous tissues. Additionally, we investigate how identifying putative biomarker gene expression patterns can aid in predicting future risk of cancer and inform the provision of personalized treatment.

Clinical Prediction Modeling in Intramedullary Spinal Tumor Surgery.

Artificial intelligence is poised to influence various aspects of patient care, and neurosurgery is one of the most uprising fields where machine learning is being applied to provide surgeons with greater insight about the pathophysiology and prognosis of neurological conditions. This chapter provides a guide for clinicians on relevant aspects of machine learning and reviews selected application of these methods in intramedullary spinal cord tumors. The potential areas of application of machine learning extend far beyond the analyses of clinical data to include several areas of artificial intelligence, such as genomics and computer vision. Integration of various sources of data and application of advanced analytical approaches could improve risk assessment for intramedullary tumors.

Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools

Nowadays, machine learning projects have become more and more relevant to various real-world use cases. The success of complex Neural Network models depends upon many factors, as the requirement for structured and machine learning-centric project development management arises. Due to the multitude of tools available for different operational phases, responsibilities and requirements become more and more unclear. In this work, Machine Learning Operations (MLOps) technologies and tools for every part of the overall project pipeline, as well as involved roles, are examined and clearly defined. With the focus on the inter-connectivity of specific tools and comparison by well-selected requirements of MLOps, model performance, input data, and system quality metrics are briefly discussed. By identifying aspects of machine learning, which can be reused from project to project, open-source tools which help in specific parts of the pipeline, and possible combinations, an overview of support in MLOps is given. Deep learning has revolutionized the field of Image processing, and building an automated machine learning workflow for object detection is of great interest for many organizations. For this, a simple MLOps workflow for object detection with images is portrayed.

Convergence analysis of online learning algorithm with two-stage step size

Abstract Online learning is a classical algorithm for optimization problems. Due to its low computational cost, it has been widely used in many aspects of machine learning and statistical learning. Its convergence performance depends heavily on the step size. In this paper, a two-stage step size is proposed for the unregularized online learning algorithm, based on reproducing Kernels. Theoretically, we prove that, such an algorithm can achieve a nearly min–max convergence rate, up to some logarithmic term, without any capacity condition.

Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?

Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). All of these facets of AI will be fundamental to the development of more autonomous actions in surgery, unfortunately, only a limited number of surgeons have or seek expertise in this rapidly evolving field. As opposed to AI in medicine, AI surgery (AIS) involves autonomous movements. Fortuitously, as the field of robotics in surgery has improved, more surgeons are becoming interested in technology and the potential of autonomous actions in procedures such as interventional radiology, endoscopy and surgery. The lack of haptics, or the sensation of touch, has hindered the wider adoption of robotics by many surgeons; however, now that the true potential of robotics can be comprehended, the embracing of AI by the surgical community is more important than ever before. Although current complete surgical systems are mainly only examples of tele-manipulation, for surgeons to get to more autonomously functioning robots, haptics is perhaps not the most important aspect. If the goal is for robots to ultimately become more and more independent, perhaps research should not focus on the concept of haptics as it is perceived by humans, and the focus should be on haptics as it is perceived by robots/computers. This article will discuss aspects of ML, DL, CV and NLP as they pertain to the modern practice of surgery, with a focus on current AI issues and advances that will enable us to get to more autonomous actions in surgery. Ultimately, there may be a paradigm shift that needs to occur in the surgical community as more surgeons with expertise in AI may be needed to fully unlock the potential of AIS in a safe, efficacious and timely manner.

Review: Application of Artificial Intelligence in Phenomics.

Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which can enable the high-throughput phenotyping of complex plant traits. The application of artificial intelligence in various domains of science has also grown exponentially in recent years. Notably, the computer vision, machine learning, and deep learning aspects of artificial intelligence have been successfully integrated into non-invasive imaging techniques. This integration is gradually improving the efficiency of data collection and analysis through the application of machine and deep learning for robust image analysis. In addition, artificial intelligence has fostered the development of software and tools applied in field phenotyping for data collection and management. These include open-source devices and tools which are enabling community driven research and data-sharing, thereby availing the large amounts of data required for the accurate study of phenotypes. This paper reviews more than one hundred current state-of-the-art papers concerning AI-applied plant phenotyping published between 2010 and 2020. It provides an overview of current phenotyping technologies and the ongoing integration of artificial intelligence into plant phenotyping. Lastly, the limitations of the current approaches/methods and future directions are discussed.

Dynamic Load Identification for Mechanical Systems: A Review

Due to the great challenges of measuring forces directly, identifying dynamic loads based on accessible responses is a crucial problem in engineering, helping ensure integrity and reliability of mechanical structures. Dynamic load identification is a difficult inverse problem due to matrix ill-posedness, noise-sensitivity and computational scale, especially in uncertain structures. Unexpected inaccurate or non-unique solutions may be found if these problems are not well addressed. During the past decades, many methods have been proposed to deal with these problems. This paper tries to provide a comprehensive review of techniques for dynamic load identification, including under ill-posedness and uncertain parameter processing; with an emphasis on the statistical, data science, machine learning, and artificial intelligence aspects. Classical physics-based dynamic load identification theories in frequency and time domain are also introduced. Research challenges and prospects of dynamic load identification in mechanical systems are discussed finally. This review may offer guidelines for dynamic load identification in practical complex structures, as well as possibilities for further researches. Some methods could have broader applicability to other inverse problems.

The Book Review Column

In this column, we review these three books: 1. Algorithmic Aspects of Machine Learning, by Ankur Moitra. A succinct book about theoretical aspects of ML. Review by Sarvagya Upadhyay. 2. Network Flow Algorithms, by David Williamson. An examination of many aspects of these important algorithms. Review by S.V. Nagaraj. 3. The Theory of Quantum Information, by John Watrous. A new, unified treatment of this vital area. Review by Steve Fenner.

Review of Algorithmic Aspects of Machine Learning By Ankur Moitra

Over the past two decades, machine learning has seen tremendous development in practice. Technological advancement and increased computational resources have enabled several learning algorithms to become quite useful in practice. Although many families of learning algorithms are heuristic in nature, their usefulness cannot be understated. Empirical observations coupled with abundance of new datasets have led to development of novel algorithmic techniques that aim to accomplish a variety of learning tasks efficiently on real-world problems. But what makes these algorithms work on such real-world problems? Clearly, producing correct solutions is one aspect of it. The other aspect is efficiency. While many of these algorithms solve hard problems and cannot be theoretically efficient (under plausible complexity-theoretic assumptions), they seemingly do work on real-world problems. It begets the question: are there conditions under which these algorithms become tractable? Having an answer to this fundamental question sheds light on the power and limitations of these algorithmic techniques. This book focuses on different learning models and problems, and sets out to capture the assumptions that make certain algorithms tractable. The emphasis is on models and algorithmic techniques that make learning an efficient endeavor.

Silas: A high-performance machine learning foundation for logical reasoning and verification

This paper introduces a new high-performance machine learning tool named Silas, which is built to provide a more transparent, dependable and efficient data analytics service. We discuss the machine learning aspects of Silas and demonstrate the advantage of Silas in its predictive and computational performance. We show that several customised algorithms in Silas yield better predictions in a significantly shorter time compared to the state-of-the-art. Another focus of Silas is on providing a formal foundation of decision trees to support logical analysis and verification of learned prediction models. We illustrate the potential capabilities of the fusion of machine learning and logical reasoning by showcasing applications in three directions: formal verification of the prediction model against user specifications, training correct-by-construction models, and explaining the decision-making of predictions.

Preface

On behalf of the Organizing Committee of ICMLSC-2020, we are delighted to welcome you to the 2nd International Conference on Machine Learning, Security and Cloud Computing. Not only does this conference set out to bring together some of leading academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects of Machine Learning, Security and Cloud Computing but also it sets the tone for further research.What this conference is intended to do is to provide a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of Machine Learning, Security and Cloud Computing.The primary focus of ICMLSC-2020 is to blend both the theory and the applications Machine Learning, Security and cloud computing. This conference is bound to bring together the academia, researchers, developers and practitioners from scientific organizations and industry to share and disseminate recent research findings in the fields of Machine Learning, Security and cloud computing and its applications. ICMLSC-2020 is an outstanding platform to discuss the key findings, exchanging novel ideas, listening to the world-class leaders and sharing experiences with peer groups.List of EDITORS are available in this pdf.

REAL- TIME ATTENDANCE SYSTEM USING FACE RECOGNITION TECHNIQUE

With increasing technologies and scientific knowledge, today’s world has resulted in a great change in almost all aspects. Technical facilities, machine learning, algorithms and other aspects are playing a huge role in almost every part of the world. Taking this into consideration, this research was developed by us, which includes face recognition, face detection and feature extraction. This research is based on real time face recognition for attendance, as it may help in huge number of institutions and other sectors. Here no physical involvement of invigilator is required. The system will totally conduct the processes based on better internet connection and better illumination. An extra feature has been added which includes the details of the pupil to be emailed to their guardian. It’s undertaking is done with Python 3.7.6, OpenCV 3.4. and Anaconda Navigator(Anaconda3). The proposed arrangement is tried for different light intensities and conditions.

Return on Investment in Machine Learning: Crossing the Chasm between Academia and Business

Abstract Academia remains the central place of machine learning education. While academic culture is the predominant factor influencing the way we teach machine learning to students, many practitioners question this culture, claiming the lack of alignment between academic and business environments. Drawing on professional experiences from both sides of the chasm, we describe the main points of contention, in the hope that it will help better align academic syllabi with the expectations towards future machine learning practitioners. We also provide recommendations for teaching of the applied aspects of machine learning.

Artificial Intelligence in cardiovascular medical imaging

Artificial Intelligence (AI) has become an important tool for computer-aided diagnosis of medical imaging. This review aims to provide an overview for clinicians, explaining the relevant aspects of artificial intelligence and machine learning (ML) and presenting up-to-date applications of AI and ML techniques to medical imaging methods such as angiography, magnetic resonance, and echocardiography. For each imaging method, we present the acquisition process, the types of diagnostic test interpretation, and the challenges related to them, as well as how AI/ML techniques, have improved the process of decision making. A summary of selected works applying AI/ML techniques to medical imaging is organized into a table, which highlights the scope of the study, the dataset used, the details of each approach as well as the measured results, including objectives and criteria. The overall benefits of AI in medical imaging are extracted based on the diverse applications and high evaluation scores. In the end, cardiologists should have an advanced understanding of using AI to integrate clinical data and making the final decision in diagnosis.

How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts.

In recent years, there has been a dramatic increase in research papers about machine learning (ML) and artificial intelligence in radiology. With so many papers around, it is of paramount importance to make a proper scientific quality assessment as to their validity, reliability, effectiveness, and clinical applicability. Due to methodological complexity, the papers on ML in radiology are often hard to evaluate, requiring a good understanding of key methodological issues. In this review, we aimed to guide the radiology community about key methodological aspects of ML to improve their academic reading and peer-review experience. Key aspects of ML pipeline were presented within four broad categories: study design, data handling, modelling, and reporting. Sixteen key methodological items and related common pitfalls were reviewed with a fresh perspective: database size, robustness of reference standard, information leakage, feature scaling, reliability of features, high dimensionality, perturbations in feature selection, class balance, bias-variance trade-off, hyperparameter tuning, performance metrics, generalisability, clinical utility, comparison with traditional tools, data sharing, and transparent reporting.Key Points• Machine learning is new and rather complex for the radiology community.• Validity, reliability, effectiveness, and clinical applicability of studies on machine learning can be evaluated with a proper understanding of key methodological concepts about study design, data handling, modelling, and reporting.• Understanding key methodological concepts will provide a better academic reading and peer-review experience for the radiology community.

Research of approaches and methods of applying artificial intelligence and machine learning in socio-economic processes

One of the problems hindering the development of the socio-economic sphere in the innovative direction is the lack of structuring of approaches and methods used in machine learning as part of the introduction of artificial intelligence (AI) in socio-economic processes. The same problem hinders the growth of the pace of innovative development and, as a result, the improvement of the scientific and technical level of the country. The article classifies and systematizes aspects of machine learning, focuses on the need to accelerate the construction and implementation of algorithms as the basis of AI for increasing the efficiency of managing socio-economic processes. To achieve this goal, the following results are presented: analysis of the concepts of machine learning and AI, study of analytical materials regarding approaches and methods to the introduction of artificial intelligence and prospects for its application in socio-economic processes. There were systematized approaches to machine learning introduction to artificial intelligence depending on the historical period, the implementation of AI, and another, and methods according to the method of machine learning, predictive model data for creating AI algorithms (e.g., probabilistic), and the idea or the nature of the research that uses this technology (assessment and collection of statistical indicators, analysis). The study of the material related to machine learning and AI construction allowed us to draw the following conclusions. The theoretical foundation in the form of mathematical and statistical methods as the basis for building algorithms for creating AI in the framework of machine learning is a necessary part of the process of teaching computers human qualities. However, information about machine learning methods and approaches is mostly scattered, and it is necessary to form a unified methodological base in order to simplify the stage of searching for the right method of creating AI to solve any social, economic or other problem. The presence of such a database will create opportunities to replace one machine learning method for creating AI with another in different fields of activity and socio-economic processes.