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Current research in artificial intelligence has been primarily focused on machine learning as applied to specific computer and engineering problems. For example, facial recognition or malware detection. This aspect of artificial intelligence has seen numerous advances and continues to advance. Research regarding synthetic consciousness has not progressed on par with research into other sub domains of artificial intelligence. While there has been speculation regarding the eventuality of some level of consciousness being developed via artificial intelligence, no specific research modalities have been adequately developed. This paper focuses on two synthetic consciousness issues. The first issue is to provide a basis for developing synthetic consciousness. The second issue is outlining specific research modalities that have a significant probability of achieving synthetic consciousness. The premise of this paper is to outline how to most effectively create algorithmic systems that have a high probability of leading to the emergent property of consciousness. [ABSTRACT FROM AUTHOR]

Recently, there has been increased research on the topic of artificially intelligent programs having the capability of developing advanced systems that are presently used by governments and organizations to analyze highly complex structures across sectors in ways not possible with conventional information technology. While some AI is subject to rigid testing and ethical reviews, other applications raise questions as to what governance structures are in place to control the risks to humanity and long term harmful economic and social consequences. This paper raises awareness about how governments and private industry face an unprecedented challenge in managing these complex systems that include regulators, markets, and special interests that all play a role in influencing the development of AI in different contexts without a full appreciation of the impact of AI on human rights and other consequences. The research focuses on three primary areas: (1) How AI technologies have evolved; (2) What are the major ethical and human rights issues evolving from the use of AI in the public and business environment; and (3) how can we improve our frameworks and governance structure for AI regulation. Through empirical evidence this paper explores the legal implications including the rights and duties of the government and private industry in protecting against unlawful intrusions into people's lives, while at the same time advancing recommendations for accountability frameworks and regulations essential to ensure safety and security in advancing artificially intelligent systems. [ABSTRACT FROM AUTHOR]

Kidney is one of the significant organs in the human body which performs filtering out blood, balances fluid, removes the waste, maintains the level of electrolytes and hormone levels. So, any disorder or dysfunction in kidney needs to be detected on time in order to preserve life. Segmentation on kidney tumor in medical field is a critical task and many conventional methods have been employed for early prediction of kidney abnormalities but with limitations such as high cost, extended time for computation and analysis with huge amount of data. Due to all such problems, the prediction rate and accuracy has reduced considerably. In order to overcome the challenges, Artificial Intelligence (AI) technology has penetrated into the field of medicine particularly in the renal department. The evolution of AI in kidney therapies improve the process of diagnosis through several Machine Learning (ML) and Deep Learning (DL) algorithms. It has the capability of improving and influencing on the status with its capacity of learning from the massive data and apply them accordingly to differentiate on the circumstances. The storage of larger data and segmentation with AI assistance are highly helpful for the analysis of occurrence of the disease. AI algorithms have predicted the severity of tumor stages with effective accuracies. Hence, this paper provides a critical review of different AI based algorithms being used in the kidney tumor prognostication. Its numerous benefits in field of segmentation have been researched from the existing works and provides an insight on the contribution of AI in the kidney disease prediction. [ABSTRACT FROM AUTHOR]

Accurate identification of the QRS complex is critical to analyse heart rate variability (HRV), which is linked to various adverse outcomes in premature infants. Reliable and accurate extraction of HRV characteristics at a large scale in the neonatal context remains a challenge. In this paper, we investigate the capabilities of 15 state-of-the-art QRS complex detection implementations using two real-world preterm neonatal datasets. As an attempt to improve the accuracy and reliability, we introduce a weighted ensemble-based method as an alternative. Obtained results indicate the superiority of the proposed method over the state of the art on both datasets with an F1-score of 0.966 (95% CI 0.962-0.97) and 0.893 (95% CI 0.892-0.894). This motivates the deployment of ensemble-based methods for any HRV-based analysis to ensure robust and accurate QRS complex detection. [ABSTRACT FROM AUTHOR]

Megaprojects are continuing to capture worldwide attention from India’s Smart Cities, to the $4.75 billion Hadron Collider at CERN, to the US $150 billion International Space Station, and Europe’s largest railway infrastructure project, Crossrail in London. The Organization for Economic Cooperation and Development (OECD) estimates global investment needs of $6.3 trillion per year from 2016-2030 (Mirabile, et al. 2017). To meet this growing demand, there has been a recent call, within the megaproject scholarship, for a better understanding of “what goes on in megaprojects – how they are managed and organized, from within, by the managers who are tasked with bringing them to fruition.” (Söderlund, et al. 2017). Studies about megaprojects have generally concentrated on the cost conflicts between the stakeholders and cost overrun issues (Adam et al., 2014; Flyvbjerg, 2017), however these have been superseded by more important issues in recent years such as project security, protecting the health and safety of its workers, project sustainability and value creation, and managing the impact of climate change and presently a global pandemic. All of these require a more agile approach to project management and a more sophisticated intelligence that can be generated through Algorithms that are used to generate artificially intelligent systems. Through an analysis of the AI and Project Management literature, ethnographic studies, and semi-structured interviews with project management professionals, this paper explores the growing use of Artificial Intelligence to manage megaprojects including the obligations of private industry, and the government as the guardian of the public interest, while at the same time exploring the technical, managerial and ethical considerations in the deployment of AI. [ABSTRACT FROM AUTHOR]

As a general rule, for pecuniary and regulatory reasons, commercial banks assiduously manage the credit risk of their loan portfolios. Algorithmic decision-making (ADM) systems may enable lenders to arrive at credit decisions that previously would not have been possible. However, the point at which utilizing ADM is optimized is still open for debate. To help illuminate the issues, a systematic literature review is conducted to investigate the following questions: How does algorithmic decision-making (ADM) contribute to the effectiveness of credit risk assessment (CRA)? And, what, if anything, can be done to improve the contribution of ADM? The review indicates that ADM’s contributions have largely been through enhanced human decision-making under uncertainty. In addition, the review underscores the importance of organizational arrangements to the successful deployment of ADM systems. Furthermore, the technology’s contribution to CRA can be improved by addressing algorithmic bias and transparency issues. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) is the study and the usage of the mathematical algorithms which can improve their performance without the need for human interaction. These algorithms are considered as a subset of Artificial Intelligence (AI). Machine learning algorithms use past data as input and produce new predicted values as an output. Machine learning algorithms have been used in many areas for solving an innumerable number of tasks. However, the various tasks need applying of different machine learning algorithms for obtaining maximum accuracy of the target results. In this paper, an analysis with consideration of the advantages, disadvantages, and different areas of applications in the real world are made for each of the four ML algorithm groups - supervised, unsupervised, semi-supervised, and reinforcement learning. After the comparative analysis is done, the ensemble methods boosting, stacking, and bagging are introduced, described, and compared. Emphasis is done on defining the accuracy of which ML algorithms can be improved and which ensemble methods can be used for that. Machine Learning algorithms combined with ensemble methods are highly competitive and provide the best results in most cases where they are applicable. [ABSTRACT FROM AUTHOR]

Machine learning is challenging the way we make music. Although research in deep generative models has dramatically improved the capability and fluency of music models, recent work has shown that it can be challenging for humans to partner with this new class of algorithms. In this paper, we present findings on what 13 musician/developer teams, a total of 61 users, needed when co-creating a song with AI, the challenges they faced, and how they leveraged and repurposed existing characteristics of AI to overcome some of these challenges. Many teams adopted modular approaches, such as independently running multiple smaller models that align with the musical building blocks of a song, before re-combining their results. As ML models are not easily steerable, teams also generated massive numbers of samples and curated them post-hoc, or used a range of strategies to direct the generation, or algorithmically ranked the samples. Ultimately, teams not only had to manage the "flare and focus" aspects of the creative process, but also juggle them with a parallel process of exploring and curating multiple ML models and outputs. These findings reflect a need to design machine learning-powered music interfaces that are more decomposable, steerable, interpretable, and adaptive, which in return will enable artists to more effectively explore how AI can extend their personal expression. [ABSTRACT FROM AUTHOR]

Optimizers are approaches or algorithms dependent to enhance the characteristics of the Neural Network (NN) like weights and learning rate in order to decrease the loss rate, On the other hand, Siamese Neural Network (SNN) are two identical sub-networks, they work in parallel and they are sharing parameters and weight, SNN uses for indicate similarity. In this research, we study the effect of optimizers Siamese Neural Network, using Digits handwritten (MINST) dataset, the effects is studied for Adam, Nadam, Adadelta and SGD optimizers with respect to process time and accuracy, the accuracy is 97%, 97%, 79% and 92%. [ABSTRACT FROM AUTHOR]

Recently, the world has a treasure of data; health data is one of them. Artificial intelligence (AI), especially machine learning (ML), is required to analyze these data and construct the associated innovative applications intelligently. There are several kinds of algorithms, including unsupervised, supervised, reinforcement and semi-supervised learning. Deep learning, which is component of a wider family of machine learning technologies, can also examine massive volumes of data successfully. This study aims to review various different healthcare disease prediction strategies such as single techniques, hybrid methods, and hybrid ensemble approaches in machine learning and deep learning. [ABSTRACT FROM AUTHOR]

The lack of transparency and explainability hinders the clinical adoption of Machine learning (ML) algorithms. While explainable artificial intelligence (XAI) methods have been proposed, little research has focused on the agreement between these methods and expert clinical knowledge. This study applies current state-of-the-art explainability methods to clinical decision support algorithms developed for Electronic Medical Records (EMR) data to analyse the concordance between these factors and discusses causes for identified discrepancies from a clinical and technical perspective. Important factors for achieving trustworthy XAI solutions for clinical decision support are also discussed. [ABSTRACT FROM AUTHOR]

In this paper, by using machine learning techniques, we are going to investigate the ability of species' spatial and spatiotemporal distribution information in an individual-based ecosystem simulation (Ecosim) for speciation prediction. Because of the imbalanced nature of our dataset we use smote algorithm to make a relatively balanced dataset to avoid dismissing the minor class samples. Experimental results showr very good results for the test set generated from the same run as the learning set. It also shows good results on test sets generated from different runs of Ecosim. We also observe superior results when we use, for the learning set, a run with more species compare to a run with less species. Finally we noticed that spatial information is very effective in speciation prediction and spatiotemporal information can improve it. [ABSTRACT FROM AUTHOR]

This article provides a complete classification of attacks using artificial intelligence. Three main identified sections were considered: attacks on information systems and computer networks, attacks on artificial intelligence models (poisoning attacks, evasion attacks, extraction attacks, privacy attacks), attacks on human consciousness and opinion (all types of deepfake). In each of these sections, the mechanisms of attacks were identified and studied, in accordance with them, the methods of protection were set. In conclusion, a specific example of an attack using a pretrained model was analyzed and protected against it using the method of modifying the input data, namely, image compression in order to get rid of extraneous noise. [ABSTRACT FROM AUTHOR]

The sheer volume of social media posts is forcing social platforms to supplement human-based fake news detection with machine-learning algorithms. Hence, this study investigates algorithm aversion in the context of fake news detection. Based on an empirical study with 238 subjects using artificial fake social media posts, our results suggest that fake news flags have a weaker warning effect if the warning contains additional information on the method used to detect fake news using either human expert opinions or artificial intelligence (AI). Algorithm aversion was reflected in participants assuming that AI algorithms erroneously classify posts as fake more often than human experts do. Furthermore, results showed that flags based on human experts have a more substantial effect on user behavior than neutral flags. These insights can practically be used to assist social media platforms in how to inform their users about the underlying method used to detect fake news. [ABSTRACT FROM AUTHOR]

Literature on algorithmic bias identifies its source in either biased data or statistical methods, more rarely in the development of algorithmic solutions as a potential factor. Because of the prior unknowability of algorithms, data scientists developing such solutions have to take various design decisions. Drawing from the flow-oriented approach, we study algorithmic unknowability and how data scientists respond to it in 35 public data science Jupyter notebooks containing algorithmic solutions to predict customer churn in a credit card dataset on a data science platform Kaggle.com. We offer a more thorough understanding of the unknowability in algorithmic development that can enable algorithmic bias: resource, problem, dataset, analytical, model, and performance unknowability. We find that in response, data scientists engage in biasenabling interpretation, bias-enabling optionalizing, and bias-enabling experimentation. These findings contribute to literature on algorithmic bias and can help avert bias earlier in practice. [ABSTRACT FROM AUTHOR]

The increase of transferred information amounts brings the need to create new automated systems for data recognition, classification and routing. It is impossible to solve such problems without modern machine learning technologies and artificial intelligence technologies. An algorithm that takes into account the linguistic and syntactic properties of documents, the characteristics of the subject area is formulated on the basis of the system approach to the analysis and decomposition of the task of document routing in the electronic document management system of scientific and educational institutions. This let to extract, process, and recognize senders and recipients of documents using machine learning methods with greater efficiency and accuracy. On the basis of the received data the subsystem of automatic routing of documents in system of electronic document management is formed. The obtained scientific results are applicable to the development of automatic routing systems in electronic document management systems. The presented approaches to the recognition and processing of recipients and senders of documents and text analysis are used to solve the problems of routing documents in various subject areas. [ABSTRACT FROM AUTHOR]

Conventional linear & nonlinear tools for classification, regression & data driven modeling are being replaced on a rapid scale by newer techniques & tools based on artificial intelligence and machine learning. While the linear techniques are not applicable for inherently nonlinear problems, newer methods serve as attractive alternatives for solving real life problems. Support Vector Machine (SVM) classifiers are a set of universal feed-forward network based classification algorithms that have been formulated from statistical learning theory and structural risk minimization principle. SVM regression closely follows the classification methodology. In this work recent applications of SVM in Chemo & Bioinformatics will be described with suitable illustrative examples. [ABSTRACT FROM AUTHOR]