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Blockchain is an emerging technology with Big data, Artificial Intelligence, and Machine Learning. It disrupted industries such as health, education, manufacturing, and banking. However, the increasing popularity of Blockchain ex- poses the scalability issues of major public blockchain platforms (e.g., Bitcoin and Ethereum) and dramatically affects its development. The scalability problem manifests in terms of Low throughput, high transaction latency, and massive energy consumption. Several reviews and studies cover these factors and their potential solutions, yet these studies need to highlight more information through actual application to natural systems or projects. This study investigates all relevant papers on current research solutions for public blockchain scalability issues. The scope of this paper is to explore the implementation of different state-of-the-art scalability solutions to natural systems and projects while simultaneously highlighting the results. This study discusses the methods and techniques used and the challenges encountered that have yet to future researchers must explore. [ABSTRACT FROM AUTHOR]

Emotion AI deals with the sentiments of human beings at various domains. It is named emotion AI as using the capabilities of AI the emotions of humans would be interpreted and analysed. The objective of this paper is to learn from the experience of patients towards the healthcare facilities by studying and analysing the sentiments of the patients using machine learning. This paper focus on training the machine for reading the reviews given by patients who have used various healthcare facilities. The machine will be trained to understand the polarity for each healthcare facility in terms of cleanliness, availability of doctors, interaction of doctors with patients etc. The code is implemented in python with various libraries required for machine Learning. The code is able to extract the polarity and is able to handle the emotions of the patients for questions answered in the dataset by patients. The paper would contribute and help the patients decides which healthcare facility they shall choose based on the experiences of various other patients. For this the goodness score for every healthcare facility is calculated and implemented using Machine learning. It's the contribution of artificial Intelligence and machine learning for healthcare Domain. [ABSTRACT FROM AUTHOR]

Diabetes mellitus remains a global health challenge, requiring innovative solutions for effective disease management. This paper offers a thorough analysis of diabetes technologies, highlighting their various roles in diabetes care. Through a thorough review of the literature and analysis of emerging trends, we explore the multifaceted impact of technology on diabetes care. We investigate the key role of continuous glucose monitoring systems, insulin pumps and smart insulin pens in achieving optimal glycaemic control. The paper also evaluates the integration of artificial intelligence and machine learning algorithms in predictive modelling for early detection of glucose fluctuations, ultimately preventing diabetes-related complications. Additionally, studies the potential of telemedicine and mobile applications in enhancing patient engagement and self-management. Moreover, the review covers advancements in closed-loop insulin delivery systems, offering insights into their clinical effectiveness and potential to revolutionize diabetes care. Ethical and privacy considerations related to the use of patient data in these technologies are discussed, emphasizing the importance of striking a balance between technological innovation and patient security. This paper's evidence synthesis underscores the increasing influence of diabetes technologies on patient outcomes, quality of life, and healthcare systems. It underscores the need for multidisciplinary collaboration between healthcare professionals, researchers and technology developers to ensure the seamless integration and accessibility of these tools to patients living with diabetes. This study serves as a valuable resource for clinicians, researchers, and policymakers, providing a comprehensive view of evolving diabetes technologies and their potential in the field. [ABSTRACT FROM AUTHOR]

The rapid growth of e-commerce platforms has emphasized the significance of personalized recommendation systems in enhancing user engagement and satisfaction. This research paper presents the development and evaluation of an innovative Product Recommendation System that leverages advanced Artificial Intelligence (AI) techniques to provide tailored product suggestions. The primary objective is to create a user-centric experience by integrating an AI assistant, enabling natural and interactive interactions. Through a comprehensive survey conducted to understand customer behaviour while purchasing product using AI, the study aims to assess the system's effectiveness in delivering accurate recommendations and providing a seamless purchasing experience. The paper contributes to the field by showcasing the practical implementation of AI-driven recommendation systems, highlighting their potential to transform e-commerce interactions. [ABSTRACT FROM AUTHOR]

The increasing exploitation of Artificial Intelligence (AI) technologies has enabled the design of user interfaces in a way that integrating artificial intelligence capabilities has become crucial in the modern digital landscape. Exploring the main features and best practices for designing user interfaces for Web applications, which effectively support and leverage AI functionalities, is currently one of the relevant topics in this context. This research work discusses the fundamental principles of user interface (UI) design, and the challenges posed by the integration of AI into web applications. It emphasizes the need to strike a balance between the AI advanced capabilities and the users' ability to understand and control the system. Furthermore, the paper highlights the importance of creating intuitive and engaging UI designs that empower users to interact with AI-driven features effortlessly. The study presents a comprehensive analysis of various UI design techniques specifically tailored for AI-enabled web applications user interfaces. Additionally, the paper explores the incorporation of AI-driven recommendation systems, personalized interfaces, and adaptive designs, which dynamically adapt to users' preferences and behavior. To validate the proposed user interface design principles, the study presents a proposal for a guidelines structure that promotes empirical evaluations through user studies and usability testing. Results collected via a survey based on measuring the effectiveness and user satisfaction of AI-enabled Web interfaces. User interfaces in real-life scenarios are presented and provides information on the impact of UI design decisions on user interaction and overall experience. The outcomes of this research work contribute to a deeper understanding of UI design for AI-supported Web applications user interfaces and offer practical guidelines for designers and developers. By embracing the suggested principles, organizations and designers can create Web interfaces that effectively harness the power of AI while prioritizing user-centricity, accessibility, and ethical considerations. [ABSTRACT FROM AUTHOR]

Digital transformation is a new trend that describes enterprise efforts in transitioning manual and likely outdated processes and activities to digital formats dominated by the extensive use of Industry 4.0 elements, including the pervasive use of cyber-physical systems to increase efficiency, reduce waste, and increase responsiveness. A new domain that intersects supply chain management and cybersecurity emerges as many processes as possible of the enterprise require the convergence and synchronizing of resources and information flows in data-driven environments to support planning and execution activities. Protecting the information becomes imperative as big data flows must be parsed and translated into actions requiring speed and accuracy. Machine learning and artificial intelligence have become critical in supporting extensive data collection and real-time processing to assist decision-makers in configuring scarce resources. In this paper, we present four different applications that investigate issues related to the broader maritime supply chain security domain affecting the planning, execution, and performance of complex systems while exploring novel frontiers in cyber research and education. This paper will focus on Machine Learning and AI applications on Unmanned Aerial Systems and Cryptography related to Cybersecurity in Maritimes and Shipbuilding Spheres. [ABSTRACT FROM AUTHOR]

An increasing number of manufacturing companies have initiated designing and implementing AI systems in manufacturing, however, with limited success. Within our overarching research objective of establishing a methodology for the development of AI systems in manufacturing with socio-technical system consideration, this paper focuses on the early design phase of the development life cycle and aims to identify factors that are essential in the phase but whose importance has been less addressed in the manufacturing literature. To this aim, a case study was conducted adopting a design science approach. The case company was developing an ML-based anomaly detection system for a casting process. The researcher organised an AI system design workshop where participants from the company used the Human-AI design guidelines created by a leading large software company. The workshop enabled the participants to explore a wide range of design concerns. It, however, caused the confusing experience that they had to deal with too many questions simultaneously without clear guidance. Analysing this negative experience has led to identifying four design issues requiring further attention in the research. An example of these issues is that the interdependency of design decisions on operational procedures, human-machine interfaces, ML models, pre-processing, and input data makes it challenging to design these elements in isolation. The study found that a structured approach to dealing with the identified issues was currently lacking. This paper contributes to the manufacturing research community by addressing key unresolved issues in the research through highlighting practical details of designing AI systems in manufacturing. [ABSTRACT FROM AUTHOR]

Tailings dam failures cause catastrophic impact on the environment and surrounding communities. Incidences of failure in the recent past have caused industrialists and researchers to seek innovative ways for proactively managing their safety and disaster mitigation. Given Industry 4.0 technologies now available, researchers are looking to develop digital tools for cost-effective, realtime monitoring of tailings dams. However, published literature indicates that a reliable framework is still lacking. This paper proposes a framework for developing a data-driven system for monitoring tailings dam stability and early warning detection. The framework relies upon digital twin simulation and machine-learning (ML) techniques, and comprises four main components: realtime data collection, digital twin modelling, ML-based early detection and prediction, and intelligence-driven decision-support. Sensors gather real-time geophysical data from monitored structure, and the digital twin uses this data to simulate dam behaviour. ML algorithms analyse the data and simulations to enable early detection of instability and failure prediction. Literature suggests that digital twin and ML-based approaches may have advantages over traditional monitoring techniques and other AI-based methods. The paper concludes with a discussion of the framework's limitations, opportunities for improvement, and potential for application in mining and geotechnical engineering. The paper serves as a basis for model development and future research. [ABSTRACT FROM AUTHOR]

In the digital age, organizations confront the challenge of managing diverse documents efficiently while ensuring security, accuracy, and accessibility. Conventional document management approaches often must catch up, leading to inefficiencies and increased costs. This paper introduces the Intelligent Document Management System (IDMS), which employs advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Optical Character Recognition (OCR) to enhance document workflows. This research extends the capabilities of IDMS to encompass the extraction and processing of data from three important document types: medical bills, Aadhar cards, and PAN cards. The research and development efforts done in this paper have concentrated on seamlessly integrating of these models into the IDMS framework, offering a comprehensive solution for extracting and processing data from various document types. In this paper, two approaches, namely Easy OCR and a hybrid approach of combining NLP (Regular Expression) and CV (OCR) have been applied and compared. The results revealed that the proposed hybrid approach (NLPCV) is better, with higher accuracy of 97%,71 %, and 78% for hospital invoices, Aadhar cards, and PAN cards, respectively. [ABSTRACT FROM AUTHOR]

This paper presents findings on machine learning agent behavior prediction in a board game application developed by a group of students. The goal of this research is to create a model facilitating collaboration between a user and an AI to play together in the board game using a Human-in-the-Loop architecture. By injecting explainability, the aim is to enhance communication and understanding between the user and the AI agent. Featuring a competitive Artificial Intelligence (AI) based on the Proximal Policy Optimization model, this research explores methods to make AI decisions transparent for enhanced player understanding. Two predictive models, a Decision Tree (DT) and a Deep Learning (DL) classifier, were developed and compared. The results show that the DT model is effective for short-term predictions but limited in broader applications, while the DL classifier shows potential for long-term prediction without requiring direct access to the game's AI. This study contributes to understanding human-AI interaction in gaming and offers insights into AI decision-making processes. [ABSTRACT FROM AUTHOR]

Industry 4.0 has brought modernization to the production system through the network integration of the constituent entities which, combined with the evolution of information technology, has enabled an increase in productivity, product quality, optimization of production costs, and product customization to customer needs. Despite the complexity of human thought, artificial intelligence tries to replicate it in algorithms, creating models capable of processing databases with a high volume of information, and generating valuable information for decision making. Within this area, there are subfields, such as Machine Learning and Deep Learning, which, through mathematical models, define patterns to predict output data from known input data. In addition to this type of algorithm, there are metaheuristic models capable of optimizing the parameters required in Machine Learning and Deep Learning algorithms. These intelligent systems have applications in various areas such as industry, construction, health, logistics processes, and maintenance management, among others. This paper focuses on Artificial Intelligence models addressing Industry 4.0 approach. [ABSTRACT FROM AUTHOR]

Lung cancer is the 2nd most diagnosed cancer worldwide. The corresponding histopathological analysis, being both costly and time-consuming, demands the commitment of skilled professionals who, while engrossed in this task, experience constraints on their ability to attend to other crucial responsibilities. Moreover, as it is a human-driven process, mistakes may lead to incorrect diagnosis and treatment. Given the disease frequency and mortality, automated diagnostic systems, using Artificial Intelligence (AI), represent valuable improvements in diagnostic timing and overall performance. Recently, Deep Learning (DL) has been widely used for extracting features from histopathologic images approaching more accurate and expeditious analysis. With this line of research in mind, a brief review of recent technical/scientific works within the scope of lung Digital Pathology (DP) image analysis is provided in this paper, covering different computer vision tasks including classification, segmentation, and detection. Furthermore, available datasets and open-source annotation tools capable of providing support to the aforementioned DP-related tasks are also overviewed. Afterward, a summary table and a discussion around the reviewed approaches is provided, consolidating critical information such as technique/DL architecture, involved datasets, metrics, etc. From this study, it was observed that the ARA-CNN technique achieved the highest Area Under the Curve (AUC) ranging from 0.72 to 0.99 for classification. On the other hand, the multimodal-based approach, with an AUC of 0.95, performed better for the segmentation task. As for the detection task, the BCNN approach stood out, achieving a high AUC of 0.988. This review work aims to provide a comprehensive overview of recent advancements in lung DP image analysis and serves as a foundation for future research in this critical area. [ABSTRACT FROM AUTHOR]

Enterprise Information Systems (EIS) comprise components for decision-making in an organization. While traditional Decision Support Systems (DSS) rely on sophistically designed decision models, this approach has its limitations when it comes to making decisions based on large amounts of unstructured data. In this paper, we present the case of online community management, where moderators need to decide if user content (i.e., comments) can be published. We have implemented a moderation platform that utilizes Natural Language Processing (NLP) and Machine Learning (ML) to support moderators in their decision-making. From the development process and adoption of our platform, which were carried out as a Design Science Research (DSR) project, we have derived six design principles that assist in designing ML-based DSS. Our results imply that an ML-based DSS should be implemented using an open, customizable system, where decisions are made transparent and interpretable to users. Users need special onboarding and should always have the possibility to overrule the system. [ABSTRACT FROM AUTHOR]

Video consultations and telemedicine play a key role in healthcare. It is crucial to assist institutions in identifying which patients are more likely to use them, increasing the adoption rate. Even though Artificial Intelligence models are important, certain cases require a human centric approach to inform patients and doctors, ensuring trust in the tools provided. This can be achieved through interpretable Machine Learning models and Statistical Analysis. In this paper we show that even though the accuracy of the Machine Learning models used (Decision Trees, Logistic Regression, and Random Forest) was between 84%-89% on the training set and 69%-76% on the test set the models lacked good ability to identify the users. Afterwards, additional statistical analysis evidenced that the main driver to use video consultations in a sample of participations with a higher-than-average education is the presence of one or more chronic diseases. [ABSTRACT FROM AUTHOR]

Modern ransomware implement innovative techniques and tactics to bypass existing security measures. Hence, predicting and forecasting such kind of malware is crucial for enhancing the overall cybersecurity posture in an increasingly digital and interconnected world. In this paper, we propose a proactive approach for predicting ransomware attacks. We integrated a dynamic deep learning algorithm for analyzing memory-based features. This allows us to detect the existence of ransomware indicators regardless of the usage of obfuscation techniques, such as code encryption. Thus, offering advantages in terms of obfuscation resistance, realtime insights threat analysis and adaptability to evolving ransomware threats landscape. Experimental results using recent datasets demonstrate the effectiveness of the proposed approach in identifying modern ransomware samples with a weighted average of 99.98%, 99.96%, 100%, and 99.98% for accuracy, precision, recall and fl-score respectively. [ABSTRACT FROM AUTHOR]

This research leverages the potential of digital twin technology to present a novel method of predictive maintenance for 6G communication networks. With the increasing need for 6G networks to be consistently reliable and always available, this essay emphasises the need for more advanced maintenance techniques. The seamless integration of digital twin technology makes it possible to monitor, analyse, and simulate the 6G network in real time. This innovation makes it easier to use artificial intelligence to fine-tune maintenance schedules and anticipate maintenance requirements in advance. The paper fervently promotes the use of digital twin technology as a vital instrument for raising the bar for maintenance requirements in the context of 6G networks. [ABSTRACT FROM AUTHOR]

The medical field, as many others, is overwhelmed with the amount of research-related information available, such as journal papers, conference proceedings and clinical trials. The task of parsing through all this information to keep up to date with the most recent research findings on their area of expertise is especially difficult for practitioners who must also focus on their clinical duties. Recommender systems can help make decisions and provide relevant information on specific matters, such as for these clinical practitioners looking into which research to prioritize. In this paper, we describe the early work on a machine learning approach, which through an intelligent reinforcement learning approach, maps and recommends research information (papers and clinical trials) specifically for multiple sclerosis research. We tested and evaluated several different machine learning algorithms and present which one is the most promising in developing a complete and efficient model for recommending relevant multiple sclerosis research. [ABSTRACT FROM AUTHOR]

Chatbots have revolutionized human-computer interactions in various fields, driven by advancements in Artificial Intelligence. Similarly, gamification techniques have emerged as innovative methods to engage users and enhance experiences. This paper presents an analysis of the trends, applications, methods, and challenges associated with chatbots, with a particular emphasis on their capability to generate customized gamification solutions for industrial 5.0 environments. These environments are characterized by the increased use of technologies - such as Virtual and Augmented Reality - driven by the digital transformation of Industry 4.0 (I4.0). As Industry 5.0 (I5.0) builds upon I4.0, emphasizing human-technology collaborations and coexistence of humans and advanced technologies, the role of chatbots and gamification in this context becomes even more compelling. Within this research, various gamification applications and chatbot architectures, ranging from rule-based to generative models, are identified to assess their suitability for the specific needs of industrial 5.0 environments. The primary objective of this study is to explore the potential of leveraging chatbot assistance to enhance gamification experiences by considering employee specific characteristics, workplace technologies, and other relevant factors. By examining the advancements and obstacles in this field, this study aims to explore the potential of chatbots for increasing the effectiveness of gamification strategies. This integration is anticipated to lead to more immersive and personalized experiences, increased employee satisfaction, positive work environment, and enhanced skill development and performance. However, several challenges such as security, user acceptance and the need for continuous adaptation must be addressed to ensure the successful implementation of chatbot-driven gamification in I5.0 environments. The navigation through these challenges holds the key to unlocking the full potential of these transformative technologies and methods, paving the way for a more engaging and enriching work environment. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive exploration of the development of an AI-driven Gen-Alpha education guidance indicator for Oman, employing cutting-edge machine learning techniques. The primary objective is to deliver a highly personalized support system and guidance mechanism that accompanies students throughout their academic journey. The research places a spotlight on the pivotal role of Artificial Intelligence (AI) within the realm of education. It skillfully demonstrates AI's robust capabilities in accurately forecasting student performance through the effective use of data mining and advanced machine learning techniques. An integral aspect of this research is the application of AI in aiding students to navigate the involved process of subject selection for their higher education pursuits. This multidimensional process encompasses exhaustive data collection, the detailed creation of a sophisticated machine learning model, and the application of a diverse array of state-of-the-art algorithms. Among these algorithms, K-Nearest Neighbors (KNN), Decision Trees, Random Forest, and Support Vector Machines (SVM) stand out prominently. Notably, the SVM algorithm emerges as the outright winner, delivering an extraordinary accuracy rate of 77%. This remarkable achievement underscores the model's unwavering robustness and its potential to redefine the educational landscape in Oman. In essence, this paper transcends the boundaries of conventional research and offers conclusive validation of AI's revolutionary potential in reshaping educational paradigms. By facilitating data-driven decision-making, the AI-driven Gen-Alpha education guidance indicator empowers students to embark on their educational journeys well-informed and confident, thereby playing a pivotal role in advancing Oman's education system. [ABSTRACT FROM AUTHOR]

The economic crisis in Sri Lanka began in 2019 and reached a crisis point in 2022 when protesters stormed the presidential palace in Colombo. Acute food, fuel, and other item shortages, soaring inflation, prolonged power outages, and a collapsing economy with few job opportunities all contribute to the populace's unrest. Sri Lanka still faces severe economic difficulties, and no one can say when they will be resolved. This country has seen the most prominent financial collapse since its independence in 1948. Unexpected inflation levels, a close to complete exhaustion of foreign currency reserves, a shortage of medical aid, and a rising price of essential commodities are the results. Numerous compounding reasons, including tax cuts, printing money, a countrywide regulation to move to natural or biological agriculture, the 2019 Sri Lanka Easter bombings, and the repercussions of the COVID-19 epidemic, have been cited as the root cause of the disaster. As early as 2022, these protests in Sri Lanka might be traced back to the country's economic woes. The primary driver of the current financial crisis in Sri Lanka is inflation. This article will look into Sri Lanka's inflation rates beginning in 2021. The inflation Rate of Sri Lanka has been forecasted for the next 6 months (through September 2023) using time series data. We used numerous Time Series Forecasting Algorithms for this multi-step ahead forecast. The results generated by these time series forecasters were in accordion with each other. Thus, we put forward the normalized results from each of these forecasting paradigms to get a better prediction on the inflation Rates, that Sri Lanka is supposed to face shortly (6 months), keeping in consideration of the current scenario being unaltered. The authors hope the Sri Lankan Administration will use the paper's predictions to take charge, which could lead to positive changes. [ABSTRACT FROM AUTHOR]

An exponential growth of interest in the healthcare IoT over the past few years has increased the adoption of AI. However, healthcare analytics demands highly accurate and reliable systems that can not afford even a small amount of vulnerability. The black-box nature of AI models raises significant concerns about the computations involved in these predictions, leading to a lack of trust in these models. Explainable Artificial Intelligence (XAI) seeks to resolve this problem by providing a human-understandable explanation for AI decisions, bringing transparency, trust, and fairness to these AI models. This paper evaluates the existing XAI models on numeric and graph-structured healthcare data. The LIME and SHAP models are first used to explain the predictions in the numerical dataset of fetal health prediction. Second, graph-structured data is analyzed using GNN Explainer and PG Explainer. This evaluation suggests that XAI model interpretations benefit healthcare professionals and patients to trust AI predictions. [ABSTRACT FROM AUTHOR]

Abstract - Student performance prediction has a pivotal role in educational institutions, providing valuable insights into students' academic outcomes and aiding educators in implementing personalized interventions. In the previous works, many variants of ML and AI techniques have been implemented with high accuracy. However, the lack of transparency due to the heterogeneous nature of features that lead to biases, forced inequalities or even challenges in interpreting the model implications have not been addressed. An approach that harnesses the power of hybrid intelligence, combining human expertise with artificial intelligence (AI), to enhance transparency and accountability in the decision-making process of student performance prediction is required. Co-evolutionary Hybrid Intelligence (CHI) approach, will not only discover interdisciplinary facets including past exam scores, attendance records, demographic information, and socio-economic factors but also interpret the influence of every component i.e., teacher, student, and learning environment to predict student performance. Two modules are discussed in this paper. The first module classifies the teachers into two classes of expertise by comparing the accuracy performance of six well-known machine learning techniques namely Logistic Regression (LR), MultiLayer Perceptron(MLP), Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbor (kNN), and Random Forest (RF). Second, we propose a multiclass student performance prediction model (RLCHI) built upon a combination of reinforcement learning (RL) algorithms that will aid the model to perceive patterns and adjustments inherently and expert teachers' opinions(CHI) from the first model that will bring invaluably contextual understanding, nuanced judgment, and a deep knowledge of pedagogy to the equation. The performance of the RLCHI model is evaluated. The obtained results show that the proposed model scores better than models built only on the RL algorithm on the same dataset. The accuracy, precision, recall, and Fl score of the model achieved with hybrid intelligence is 79.5, 49.9, 52.3, 53.2 when compared to 71.9,48.30,46.6,46.1 achieved with only RL respectively. [ABSTRACT FROM AUTHOR]

Cybersecurity threats, particularly spam SMS, are increasingly sophisticated, demanding more advanced detection systems. Traditional spam detection methods fall short due to their ineffectiveness against novel threats and lack of transparency. This paper investigates the role of Explainable Artificial Intelligence (XAI) in spam detection, emphasizing the interpretability of AI-driven systems through SHapley Additive explanations (SHAP). We propose a hybrid model combining BERT with Random Forest (RF) and Artificial Neural Networks (ANN) for spam detection, and employ SHAP values to elucidate the decision-making process. The study demonstrates that our XAI approach not only improves the accuracy of spam detection but also enhances the transparency and trustworthiness of the predictions. These findings suggest that the incorporation of XAI into spam detection models is not only beneficial but necessary for future cybersecurity measures. Our research invites further exploration into other XAI techniques and their applications in real-world scenarios. [ABSTRACT FROM AUTHOR]

Mutual funds, a cornerstone of modern investment portfolios, offer investors a diversified and professionally managed approach to financial growth. These investment vehicles collect small amount from multiple investors and invests the collective investment further in diversified portfolio of stocks, bonds, or other securities. Mutual funds are characterized by their net asset value (NAV), which represents the per-share value of the fund's assets minus its liabilities. Investors benefit from the expertise of seasoned fund managers who make investment decisions based on their analysis of market trends and company performance. By providing an accessible and diversified investment option, mutual funds have become a popular choice for both novice and seasoned investors seeking to achieve their financial goals. Apart from that, the role of AI is increasing exponentially in various areas. Such AI based tools and algorithms are utilized by various MFs expert to identify the suitable investment portfolios. This paper proposes a technique to analyse the performance of the different stock's portfolios based on the past data. Combining the isolated analysis of different stocks portfolios may be utilized to predict the net asset value prediction (NAV) of MFs. The experiments carried out in this research work uses linear regression analysis to analyse the stock's performance at sub level of the MFs. A case study for the Axis Bluechip fund's is also carried out by using the proposed ML approach. The Yahoo Finance dataset is used for the case study. Previously, machine learning algorithms like linear regression (LR), decision tree regression (DTR), and multivariate regression (MR) are taken to predict and analyse the dataset. These algorithms calculate the NAV of the mutual fund with good accuracy and analyse the best result for the test dataset. The various models are being created from different portfolios to analyse the NAV of the mutual funds. This work proposed hierarchical method to perform the linear regression analysis for the NAV predicting. This work uses R-Square (R2) statistical measures to determine the goodness of models. The obtained R2 is 0.86, implying that the created models have good accuracy and performance on the dataset. [ABSTRACT FROM AUTHOR]

Warehouse design and planning involve complex decisions on receiving, storage, order picking and shipping products (i.e., stock-keeping units - SKUs) and can affect the performance of entire supply chains. With the advancement of Industry 4.0 and increased data availability, high-computing power, and ample storage capacity, Machine Learning (ML) has become an appealing technology to address warehouse planning challenges such as Storage Location Assignment Problems (SLAP) and Order Picking Problems (OPP) for intelligent warehousing management. This paper presents a state-of-the-art review of ML applied to Warehouse Management Systems (WMS) through the analysis of recent research application articles. A mapping to classify the scientific literature in this new research area, including ML methods, algorithms, data sources and use cases of ML-aided WMS, as well as further research perspectives and challenges, are introduced. Preliminary results suggest that the possible research areas in ML-WMS are still incipient and need to be further explored. [ABSTRACT FROM AUTHOR]

The implementation of intelligent techniques produces good results in automating fault finding and predicting future outcomes. These approaches have been on the increase in the past years, especially so to detect faults within Compressed Air Systems (CASs). With the use of intelligent techniques, one could minimise the manual and time-consuming aspect of CAS maintenance, improve the environmental impact of the system, while minimising downtime. This paper proposes a general framework for the implementation of intelligent analysis techniques within a real-world system. Such an approach has been implemented on the demand-side of a CAS. In literature, no open datasets are available for use by artificial intelligence models. Hence, as part of this research, a fault generating and monitoring system has been connected to an existing production machine in a manufacturing site to collect the required data. Two classification machine learning methods were implemented and compared across a number of performance metrics. Both the general framework, and its implementation, provide a stepping stone in integrating smart systems with real-time intelligent data analytics for the demand-side of a CAS. These systems would provide a sustainable CAS operation through the effective detection of anomalies and their timely repair. [ABSTRACT FROM AUTHOR]

Industry 4.0 uses artificial intelligence and machine learning algorithms to optimize processes. The main goal of the manuscript is to analyze the possibilities of applying automated machine learning to predict company bankruptcy. The data sample consists of financial data of 9,771 manufacturing companies for the years 2020 and 2021 collected from the Finstat database. Two methods of automated machine learning, AutoML and H2O, were tested. The results were compared with five other methods - linear discriminant analysis, logistic regression, naive Bayes classifier, CatBoost and XGBoost. The resulting model was cross-validated through the 10-fold approach. The best results were achieved by H2O automated machine learning algorithm with an AUC of 90.13%, followed by the gradient boosting methods CatBoost with AUC of 90.05% and XGBoost (AUC of 88.61%) and another automated learning algorithm AutoML with AUC of 81.17%. The findings of this paper indicate possibilities to apply automated machine learning methods in predicting bankruptcy. However, it is necessary to distinguish between individual automated machine learning algorithms since they provide a different range of results. [ABSTRACT FROM AUTHOR]

Due to environmental issues such as climate change, companies are required to optimize their resource and energy consumption in their production process. Predicting power consumption and processing time of all production facilities is essential for manufacturing to develop mechanisms to prevent energy and resource waste and optimize their use. Machine learning is a powerful tool for prediction tasks using data in digitalized environments. In this paper, we present power consumption and processing time prediction of CNC milling machines using five machine learning regression models, i.e., decision tree, random forest, support vector regression (SVR), extreme gradient boosting (XGBoost), and artificial neural network (ANN). Since most of those models are black-box, we applied two explainable artificial intelligence (XAI) approaches, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), to give post-hoc explanations of the predictions given by the machine learning models. Our experiments indicated that random forest regression performed the best in predicting power consumption and processing time. The explanation showed that the number of axis rotations and the number of travels to the machine's zero point in rapid traverse were the most important factors that affected the processing time and power consumption. The companies using CNC milling machines can use our prediction models to optimally plan and schedule the operation of the milling machines in a time and energy-efficient manner. They can also optimize the factors that affect power consumption and processing time the most. [ABSTRACT FROM AUTHOR]

Data-driven predictive maintenance (PM) is an approach that leverages advanced analytics, artificial intelligence (AI), and sensor data to predict when equipment failure might occur, and to perform maintenance just in time to prevent it, reducing downtime and maintenance costs. In manufacturing, one of the biggest potential applications for intelligent PM systems is tool condition monitoring (TCM). TCM aims to monitor tool wear in real-time, ensuring the quality of the manufactured products and the safety of the surrounding people and equipment. In recent decades, many studies have been carried out on tool condition monitoring for different machining operations such as milling, drilling or turning, and have demonstrated the effectiveness of various AI algorithms. Recent advances in hardware have made it possible for edge devices to run complex AI algorithms locally. This technology is called Edge AI. The Edge AI approach has several key benefits such as reduced latency, scalability, data privacy and security that can accelerate the integration of the PM solution for TCM at the production level. This paper presents the design of an Edge AI system for tool condition monitoring, consisting of state-of-the-art, low-cost components and using open-source software. Based on the proposed design, a prototype was built and tested during milling process. Four machine learning (ML) models and one deep learning (DL) model were run on a low performance edge device. Their predictions were validated. Challenges faced in the implementation are concluded along with directions and suggestions for future research. [ABSTRACT FROM AUTHOR]

This paper provides a method for predicting the energy efficiency of buildings using artificial intelligence tools. The scopes is twofold: prediction of the levels of the heating load and cooling load of buildings. A feature of this research is the performance of intellectual analysis in conditions of a limited amount of data when solving the stated tasks. An improved method of augmentation and prediction (input-doubling method) is proposed by processing data within each cluster of the studied dataset. The selection of the latter occurs due to the use of the fast and easy-to-implement k-means method. Next, a prediction is made using the input-doubling method within each separate cluster. The simulation of the method was performed on a real-world dataset of 768 observations. The proposed approach was found to have a high prediction accuracy in the absence of overfitting and high generalization properties of the improved method. Comparison with existing methods showed an increase in accuracy by 40-46% (MSE) compared to SVR with rbf kernel, which is the basis for the improved method, and by 5-12% (MSE) compared to the closest existing hierarchical predictor. [ABSTRACT FROM AUTHOR]

The metaheuristics approach is important for solving many engineering applications, including healthcare. In the healthcare sector, many problems related to patients' treatments are not covered, and it is one of the issues for healthcare services across the globe. Healthcare support functions like clinical decision support, patient profiling, customized care, and health tracking could be the area where data analysis effectively provides end-user support. This paper focused on the metaheuristics algorithm concept with data mining technologies to provide a healthcare sector solution mechanism. This paper identifies the data analysis problem of the healthcare sector. It reviews various recent technologies used in healthcare and related sectors. It focused on the importance of metaheuristics algorithms, artificial intelligence, and implementation methodologies. [ABSTRACT FROM AUTHOR]

Data gathering related to biological parameters evolution using sensor networks associated to the patient, correlated with the analysis and data processing, together with a priori information and knowledge related with the patient, will allow developing new digital models for the characterization of healthcare state. By the help of algorithms for automated learning or neuromorphic devices, one can build a library for patients' Digital Twins based on its corresponding health state. In this papers authors present the main components of an integrated informatic platform which assure an efficient access for patients to various medical health services. Also, the paper presents a model for each patient health state using specific artificial intelligence techniques as a support system for every family doctor's decision and medical experts. [ABSTRACT FROM AUTHOR]

The data-driven city is improving the quality of everyday life for citizens and the efficient use of resources. While data-driven cities are benefiting from artificial intelligence technologies, one of the major challenges are the security, privacy, managing issues and costs. Furthermore, the training data for machine learning methods are in many cases not sufficiently available. Simulation tools are available for the designers or administration, and synthetic data generation based on real data is a promising approach on the development of new methods. This paper reports an exploratory research, targeting data generation with agent-based simulations combined with reinforcement learning. The study is still in its starting phase, but the conceptual outline and methodology has been created. The goal of this study is to establish a model to retrieve insights into the integration from a restricted data set from Google Maps and agent-based simulations. One of the primary observations is that intelligent agents, trained/reinforced paths instead of random behavior, show vastly different behavior than randomly acting agents. Evaluate the behavior according to empirical data, mostly because of the advantage of this method for complex behavior, where it would impractical to iterate through all possible paths, resulting in methodological issues. The paper exposes the following methodical challenges. (1) Realistic data sets will be large and full of decisions that appear to be random. To verify the collected, empirical data might lead to unreliable results, as actors will, as in real life, make different decisions according to circumstance. (2) The more complex a model has to be to be equivalent to the synthetic model, the more powerful the computational resources have to be. This leads to a dilemma between the model's complexity to make results comparable, and the simplicity to make computation realistic. Outline of the papers framework: (1) The generated data set challenges explored with the goal of providing realistic synthetic data. (2) A method for integration of synthetic and real-world data based on the reinforcement learning is developed. The premise is that implementing a reinforcement learning framework on top of multi-agent systems makes it possible to understand the mobility behavior of citizens. The paper explains the feasibility of both points, showing that this approach forms a solid basis for further investigation of the synthetic data generation for recognizing the mobility behavior of the citizens and enables the researchers to investigate the usage of reinforcement learning approach on the human mobility routes. [ABSTRACT FROM AUTHOR]

This paper delivers up-to-date bibliometric research dealing with deep learning used in forecasting alternative costs in leasing. The study presents a theoretical approach that is developed by the bibliometric maps. The aim of this paper is the identification and exploration of the research gap. The study illustrates the relationships between different science areas, such as applied economics and finances, computer science, deep learning, and machine learning. The alternative costs in the literature are related to non-financial areas and the other Artificial Intelligence (AI) methods to calculate them. There is a scarcity of alternative costs calculation with deep learning procedures or their theoretical analysis. To fill this gap, this study uses VOSviewer software to explore queries' results in the Scopus database and to illustrate the knowledge about alternative costs in deep or machine learning. The results show that: (I) the number of publications tends to increase gradually; (II) prediction or forecasting of the alternative costs is connected directly neither to the lost benefits, nor costs and risk assessment; (III) most studies concern the use of statistical methods in forecasting, but only a few use machine learning algorithms or decision trees. This scientific paper serves as a starting point for future studies dedicated to the deep learning procedures to forecast alternative costs in leasing or other financial processes. [ABSTRACT FROM AUTHOR]

The presence or absence of cancer metastasis in the lymph-nodes using an AI-based approach has been important these days in the medical field of gastroenterological surgery. The medical field needs the introduction of machine learning to help the knowledge and skill of surgery of medical doctors who are taking operations by checking CT scans or MRI images for cancer disease. Recent machine learning based researches on lymph-node are mainly either detection of the location of lymph-nodes or classification of cancer metastasis. This paper proposes a method to perform both tasks at the same time with a single U-Net model. Obtained results in the experiments show that the classification accuracy is further improved compared with the previous approach while keeping the detection ratio as almost the same level as that of the related paper. [ABSTRACT FROM AUTHOR]

Explainable AI, as the word implies is a type of artificial intelligence which enables the explanation of learning models and focuses on why the system arrived at a particular decision, exploring its logical paradigms, contrary to the inherent black box nature of artificial intelligence. Similarly, machine learning interpretability allows users to comprehend the results of the learning models by providing reasoning for the decisions that it has arrived at. This nature of Explainable AI(XAI) and Interpretable Machine Learning (IML) is particularly helpful in the context of AI applications pertaining to healthcare and medical diagnosis. In this paper, we present a case study wherein we have focused on using the ELI5 XAI toolkit in conjunction with LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) algorithmic frameworks in Python, for determining if a patient is diabetic or not, based on a randomized clinical trial dataset. We also endeavor to point out trends and most vital factors that can help clinicians and researchers in analyzing patient data, in conjunction with machine learning and artificial intelligence outputs. Having explanations for machine learning models allows for higher degree of interpretability and paves the way for accountability and transparency in medical and other fields of data analysis. We explore the aforementioned paradigms in the context of this research paper, paving the way for developing an accountable, transparent and robust data analytics framework using XAI & IML. [ABSTRACT FROM AUTHOR]

Intelligent machines are an important carrier of artificial intelligence, which are widely used not only in the industrial field, but also in various fields of daily life and work. Path planning is an important research direction for studying machine intelligence. The main purpose is to search for a feasible path so that the robot can walk along the searched path in a complex environment. This paper aims to study the evolutionary algorithm of intelligent machines based on artificial intelligence. On the basis of introducing the characteristics of artificial potential field algorithm, the concept and characteristics of genetic algorithm, using genetic algorithm as the framework, the artificial potential field algorithm is combined with the crossover of genetic algorithm. In the operator, the optimization performance of the crossover operator is improved. The research results show that the length of the path obtained by the improved algorithm is shorter and better than that of the genetic algorithm of the single-point crossover operator. [ABSTRACT FROM AUTHOR]

This paper presents a Text similarity detection method based on artificial intelligence and natural language processing. The method combines statistical machine learning and deep learning techniques and designs six models from three perspectives: character-level, word-level, and semantic-level. These models include the diff model based on machine learning, cosine similarity model, Jaccard model, TF-IDF model, as well as the SimCSE and SBERT models based on deep learning methods. To fully leverage the characteristics of these models, three scenarios are designed to calculate the similarity scores based on experience and multiple experimental results. The results show that calculating the similarity scores using these three scenarios not only achieves high accuracy but also requires fewer computational resources. As deep learning and natural language processing technologies continue to advance, Text similarity detection methods based on artificial intelligence and natural language processing will continue to be improved and play a more significant role in practice. Future research can explore more models and algorithms to enhance the accuracy and robustness of plagiarism detection. [ABSTRACT FROM AUTHOR]

Intelligent technologies are widely implemented in different areas of modern society but specific approaches should be applied in services. Basic relationships refer to supporting customers and people responsible for services offering for these customers. The aim of the paper is to analyze and evaluate the state-of-the art of artificial intelligence (AI) applications in the hospitality industry. Our findings show that the major deployments concern in-person customer services, chatbots and messaging tools, business intelligence tools powered by machine learning, and virtual reality & augmented reality. Moreover, we performed a survey (n = 178), asking respondents about their perceptions and attitudes toward AI, including its implementation within a hotel space. The paper attempts to discuss how the hotel industry can be motivated by potential customers to apply selected AI solutions. In our opinion, these results provide useful insights for understanding the phenomenon under investigation. Nevertheless, since the results are not conclusive, more research is still needed on this topic. Future studies may concern both qualitative and quantitative methods, devoted to developing models that: a) quantify the potential benefits and risks of AI implementations, b) determine and evaluate the factors affecting the AI adoption by the customers, and c) measure the user (guest) experience of the hotel services, fueled by AI-based technologies. [ABSTRACT FROM AUTHOR]

Diabetes is a condition associated with high levels of blood glucose and insulin deficiency that is becoming more prevalent among children and adolescents. It is the ninth leading cause of mortality globally at over 1 million deaths per year. Misdiagnosis of type 1 diabetes with type 2 are frequent and causes diabetic ketoacidosis, a critical complication. This paper seeks for the most accurate model between frequently used machine learning algorithms - Multilayer Perceptron, Support Vector Machine and Random Forest in predicting diabetes among patients with focus on blood content analysis, specifically using the Laboratory of Medical City Hospital (LMCH) Diabetes Dataset, which was retrieved from Mendeley. Using hyperparameter optimization and ANOVA F- Value feature scaling, results show that Random Forest produced the highest accuracy at 1.00, followed by Multilayer Perceptron at 0.987 and Support Vector Machine at 0.96. [ABSTRACT FROM AUTHOR]

The technological progress in computer science (particularly, in machine learning) has contributed to the improvement of medical services, both in detecting and treating diseases. The large volumes of data, that are overwhelming for human experts (doctors, nurses), can easily be managed by automated systems, as long as we have the computational resources. Obviously, human experts are still essential in the process - we think of the use of computer science in medicine as a collaboration between medical staff and artificial intelligence. The usual types of data that can be processed by automated systems are text, sound, and image types. In this paper, we approach the diagnosis subject and focus on data consisting of sound. We created a heart murmur detection system - it analyzes recordings and tells the user whether the sound samples indicate a heart murmur or not, based on a trained machine learning model. One of the main advantages of our system is the fact that we ran a large number of experiments, with different configurations of denoising techniques and features taken into consideration. We were able to draw some interesting conclusions, for example, we found out which features are the most important for the classification and which features are not worth computing. Also, our work denotes a thorough understanding of sound processing. [ABSTRACT FROM AUTHOR]

In recent years, several software and hardware solutions have been proposed for object detection, motion tracking, and gesture identification. However, these solutions failed to efficiently identify appropriate gestures and track their motion in a stipulated time range. To overcome the above-mentioned challenges, we propose a novel sign language translator application, which uses the MediaPipe Holistic model along with Long Short-Term Memory (LSTM), integrated with Neural Network (NN) model to build the proposed translator model. This model will pick up the gestures from different angles quickly and accurately to translate them into appropriate text. This paper has been divided into two parts, the first part involves research and data collection, and the second part involves training and testing the collected data for real-time implementation. This model has been trained and tested with real-time data collected from the Leap motion controller and cameras. The model provides appreciable results. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to enhance the trustworthiness of black-box and interpretable models in performance testing through the application of Explainable Artificial Intelligence (XAI). The proposed approach utilizes the Shapley Additive Explanation (SHAP) algorithm as a surrogate model, enabling performance analysts to gain insights into the decision-making process of black-box machine learning models. By incorporating SHAP around black-box models, analysts can obtain explanations regarding the pass or fail status of a test, as well as identify the relative significance of performance data to the machine learning models. To evaluate the effectiveness of the proposed approach, load testing experiments were conducted on a real testbed, employing industry-standard benchmarks and manually injected performance bugs. The results demonstrate that the proposed approach significantly enhances the trustworthiness of machine learning models by providing interpretable explanations for their decision-making. Furthermore, the approach showcases its versatility across domains and requires minimal effort to operate, making it highly applicable in various contexts. [ABSTRACT FROM AUTHOR]

In this paper, as a new application of machine learning, a K- Nearest Neighbor (KNN) classification model is proposed to recognize artifacts in Electrocardiography (ECG) signal using 3-axis accelerometer signals. A fine KNN model using wavelet scattering coefficients as features of 3-axis accelerometer sensor captured training dataset have been proposed as alternative to other machine learning methods to classify artifacts. In wearable health-monitoring devices, artifact noise results signal deformations and it is not possible to employ filtering methods all the time due to data loss. As a result, wearable ECG monitoring devices are not reliable for critical medical usages. Our proposed model classifies accelerometer sensor samples with high probability of having artifact noises in ECG signal with positive predictive value of 94.7%. High accuracy rate of the proposed model in this work gives it an opportunity to be employed in ECG monitoring wearable devices to capture patients ECG history and labeling artifact samples for noise filtration or signal reconstruction systems to cancel the noise without having signal deformations. [ABSTRACT FROM AUTHOR]

Decision making has evolved throughout the years, nowadays harnessing massive amounts and types of data through the unprecedented capabilities of data science, analytics, machine learning, and artificial intelligence. This has potentially led to higher quality and more informed decisions based on the collaborative rationality between humans and machines, no longer bounded by the cognitive capacity and limited rationality of each on their own. However, the multiplicity of modes of collaboration and interaction between humans and machines has also increased the complexity of decision making, consequentially complicating ex-ante and ex-post decision evaluation. Nevertheless, evaluation remains crucial to enable human and machine learning, rationalization, and sensemaking. This paper addresses the need for more research on why and how to evaluate collaborative rationality-based decisions, setting the stage for future studies in developing holistic evaluation solutions. By analyzing four relevant streams of literature: 1) classical decision theory and organizational management, 2) cognitive and neuroscience, 3) AI and ML, and 4) data-driven decision making, we highlight the limitations of current literature in considering a holistic evaluation perspective. Finally, we elaborate the theoretical underpinnings from the knowledge base on how humans and machines evaluate decisions, and the considerations for evaluating collaborative rationality-based decisions. [ABSTRACT FROM AUTHOR]

K-Nearest Neighbors (KNN) are very popular supervised machine learning algorithm which can be effectively used for both multi-class classification and regression utilizing multiple features of the dataset. It trains extremely fast, that is with time complexity of O(n). However, the time complexity for generating results with KNN is O(n3dk) (where k=number of neighbours, d=dimension of data, n=number of training points) and hence it becomes very difficult to work with larger dataset. Although there are parallelizing and distributed computing techniques available for speeding up KNN, it still have limitations on horizontal scalability, fault tolerance and incremental learning capabilities. To solve these issues, the paper presents a KNN inspired novel Supervised Machine Learning algorithm called Distributed Nearest Hash utilizing unique usage of hashmap and primary key clustering order with help of wide column store databases facilitating easy incremental near-realtime scalability to train unlimited datapoints with O(n) time complexity and also predict them in near realtime with O(1) time complexity considering n >> d. This can be readily integrated in any system that uses KNN as a base classifier or regressor. The experimental results shows that the proposed DNH model is 25% faster than state of the art distributed KNN algorithm. Integrating with fuzzy logic the algorithm can be used for several class classification. Using this could aid in building very powerful smart decision making systems with very little computational power. [ABSTRACT FROM AUTHOR]

Jaundice is a condition characterized by the yellowing of skin and sclera of the eyes. Jaundice occurs when the liver is unable to eliminate the bilirubin, a waste material formed as a result of the breakdown of red blood cells. The excessive accumulation of bilirubin in the blood can result in permanent brain damage. Therefore, Jaundice has to be identified in the early stage. The diagnosis method available is a bilirubin blood test, a painful procedure where blood sample is collected from patient. To mitigate the pain, developing an alternative non-invasive approach can aid in the early diagnosis of jaundice. A lot of research has been carried out to develop a non-invasive procedure. Most of the works focused on identifying jaundice by analysing the yellowness of skin. However, yellowing of the skin is a less noticeable symptom if the patient has darker skin, but the yellowing of the sclera can be more easily identifiable. This work focuses on identifying jaundice from the sclera. There is no standard publicly available dataset for jaundice diagnosis and all the previous works are carried out by collecting data from hospitals. One of the major limitations of medical data is its limited availability. In Artificial Intelligence (AI) research insufficient data results in incorrect predictions. However, previous works in this area have not worked on increasing the volume of the dataset. Through this work, the potential of the Generative Adversarial Network (GAN) is leveraged to overcome the issue of limited medical data availability. In this paper, a hybrid approach based on computer vision and classical machine learning is developed that can accurately determine the intensity of jaundice from the yellowness of the sclera. The work addresses the challenges of limited medical dataset availability while considering the privacy of the concerned individual. [ABSTRACT FROM AUTHOR]

Traffic analysis has many purposes such as evaluating the performance and security of network operations and management. Therefore, network traffic analysis is considered vital for improving networks operation and security. This paper discusses different machine learning approaches for traffic analysis. Increased network traffic and the development of artificial intelligence require new ways to detect intrusions, analyze malware behavior, and categorize Internet traffic and other security aspects. Machine learning (ML) shows effective capabilities in solving network problems. A review of the techniques used in the traffic analysis is presented in this paper. [ABSTRACT FROM AUTHOR]