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In scientific social networks, group information has become an important auxiliary information to enhance the performance of paper recommendation, as many researchers prefer to obtain interested papers by joining groups. However, the existing paper recommendation methods failed to make full use of group information. In this paper, a paper recommendation method considering group information with multi-graph attention fusion network (GI-MGAF) is proposed. Specifically, in the graph construction layer, we construct researcher-paper bipartite graph, group-researcher bipartite graph and group-paper bipartite graph. In the information propagation layer, graph attention networks (GAT) are used to learn the node representations on the constructed bipartite graphs. In the information fusion layer, the researcher-level attention and paper-level attention are developed to respectively fuse the representations of researchers and papers. Experiments were conducted on the real world CiteULike dataset and the results demonstrate the effectiveness of the proposed GI-MGAF method. [ABSTRACT FROM AUTHOR]

This paper investigates the effectiveness of sentiment analysis (SA) methods, ranging from rule-based approaches to deep learning architectures, in analysing textual data. The study focuses on three Python libraries: TextBlob, VADER, and Flair, evaluating their accuracy on a public dataset of Twitter posts. Additionally, custom neural network architectures are developed to optimize sentiment classification. Results indicate that while rule-based libraries offer simplicity, deep learning-based libraries show promise for higher accuracy. The customized LSTM models, particularly LSTM2 with architectural adjustments and regularization techniques, demonstrate improved performance over baseline models with classification accuracy as high as 76.3%. [ABSTRACT FROM AUTHOR]

This study is about sign language translation into text; this technology is for those experiencing difficulty in communicating, i.e., those who have speech disorders, hearing impairment, and are deaf. Sign language translation into text is done through a process when one uses sign language that could be translated into text. Deep learning is a machine learning and artificial intelligence (AI) technique that mimics how humans acquire knowledge; the complexity and abstraction of deep learning algorithms are stacked in a hierarchy. The researchers utilized Convolutional Neural Networks (CNN), Connectionist Temporal Classification (CTC), and Deep Belief Network (DBN) to collect and gather the results of their system through different methods of translating sign language into text. Through the systematic literature review of various published papers related to sign language translation using deep learning techniques, this paper discusses and systematically analyzed various studies and come up with a proposed model in translating sign language into text. [ABSTRACT FROM AUTHOR]

Neural networks (NNs) have proven to be a very powerful tool both for one-dimensional (1D) and two-dimensional (2D) direction of arrival (DOA) estimation. By avoiding complex and time-consuming mathematical calculations, NNs estimate DOAs almost instantaneously. This feature makes them very convenient for real-time applications. Further, unlike the well known MUSIC algorithm, neural network-based models provide accurate directions without additional calibration procedure of antenna array and a priori knowledge of the number of sources. In this review paper, the results achieved by the research group at the Faculty of Electronic Engineering in Nis are presented. The problem of DOA estimation of narrowband signals impinging upon different configurations of antenna arrays is addressed. Both Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are considered, and their advantages and disadvantages are discussed. To improve the resolution of DOA estimates, sectorization model is introduced. As shown in this work, neural network-based models demonstrate high-resolution localization capabilities and much better efficiency than the MUSIC. [ABSTRACT FROM PUBLISHER]

The relationship between online reviews and a firm declaring bankruptcy may seem unlikely. However, notwithstanding the lack of quality in relation to products or services, there is a strong anecdotal commentary on how firms lose business due to bad reviews, despite the perceived high quality of products. As such, this short paper attempts to evoke a critical dialogue on the question, "do instantiated or malicious review cause firms to lose business beyond recovery?" The paper provides exploratory evidence and attempts to provide a theoretical basis that explains this potential issue faced by millions of businesses around the world and contributes to identifying any possible pattern between negative online reviews and bankruptcy, theoretical foundations, and provides practical suggestions to mitigate the risk of bankruptcy for businesses by better managing the influx of negative reviews. Further, this paper will identify avenues for future research. [ABSTRACT FROM AUTHOR]

Sustainability assessment is a relevant issue nowadays and applies to various sectors, including healthcare. However, sustainability assessment requires dedicated tools and methods depending on the domain issue. Hence, this paper proposes a conceptual and structural framework for measuring the social sustainability of healthcare systems. The paper provides a new perspective on a complex assessment of the fundamental aspects of social sustainability with equity, quality, responsiveness, financial coverage, and adaptability in health systems. The approach that employs the multi-criteria PROMETHEE II method, based on the strong sustainability paradigm, assists health policy stakeholders in decision-making. The results demonstrate that healthcare systems in countries located in Northern and Western Europe, such as Norway, Germany, Netherlands, Belgium, Iceland, and Sweden, are the most socially sustainable. At the opposite end of the ranking are countries in Central and Eastern Europe: Poland, Latvia, Hungary, and Slovakia. [ABSTRACT FROM AUTHOR]

The prediction of disease can facilitate early intervention, comprehensive diagnosis and treatment, thereby benefiting healthcare and reducing medical costs. While single class and multi-class learning methods have been applied for disease prediction, they are inadequate in distinguishing between primary and secondary diagnoses, which is crucial for treatments. In this paper, label distribution is suggested to describe the diagnosis, which assigns the description degree to quantify the diagnosis. Additionally, a novel hierarchical label distribution learning (HLDL) model is proposed to make fine-grained predictions based on the hierarchical classification of diseases, taking into account the relationship among diseases. The experimental results on real-world datasets demonstrate that the HLDL model outperforms the baselines with statistical significance. [ABSTRACT FROM AUTHOR]

An analysis presented in the paper reveals that most equipment requires a DC power supply (low voltage and low current) rather than an AC power supply, like computers and peripherals, audio-video players, food preparation. Moreover, there is the possibility that through minimal adaptations and other equipment they can operate in DC microgrids. A validation for one or another microgrid types, when the AC grid has priority, is based on an in-depth study. There are currently consumers (especially in the home appliance field) that can be used successfully in bothmicrogrids (AC and DC). This category of receivers includes AC electrical light sources, which study and analysis of compatibility in DC network, will be detailed in the paper. The research results conduct to the conclusion that most of the AC consumers used nowadays, are easy to use and adapt to a DC microgrid, like the residential low voltage electric network. Paper will focus only to a detailed analysis for one lightning source. [ABSTRACT FROM AUTHOR]

This paper describes a case study of a single-family home to apply Six Sigma Problem Solving Methods and tools to reduce the electricity Bill and usage. The goal of the project was to identify and implement improvements that would reduce electricity usage and hence reducing electricity bill. The project team applied the Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) problem solving approach. Six Sigma is a framework to improve performance Process or Tasks. The author of the paper has been working on the topic over the past 1 year. The author was able to reduce the electricity bill by 50-60% by making some proper research and lifestyle change. The highest bill that was noted is $135 a month and lowest after performing the control measures came down to $57 a month. [ABSTRACT FROM AUTHOR]

Revolution in medical imaging systems generate high-resolution images. This revolution has created new challenges in accessing, sharing, archiving, and protecting medical images. The number of these images has also increased significantly over the past decade, which is create another challenge. Recently, the health care industry has started noticing cloud computing as a solution because of its elasticity and scalability, which contribute to avoiding hardware obsolescence, providing universal access to medical images anywhere and anytime, and increasing their availability. This paper introduces the main characteristics of the cloud-based picture archiving and communication system (PACS) and highlights its technical and nontechnical advantages. Moreover, this paper illustrates challenges in cloud-based medical image exchange, regulation, and interoperability as well as how medical image existence can affect cloud users. Furthermore, the paper concludes with directions for future research in medical imaging. [ABSTRACT FROM AUTHOR]

Nowadays, companies' first target is to build an intelligent, flexible, integrated and efficient processes, for higher industrial performance. According to this, a new concept emerged within the industrial firms by the name of Industry 4.0 (I4.0). This 4th industrial revolution covers four main aspects: intelligent factory, real-time communication, smart products and more satisfied customers. As strong as are the new technologies offered by this revolution, it is not entirely sufficient if not combined with lean principles. Combining these two paradigms created what is known today as Lean 4.0, a new concept presenting several opportunities for higher performance. Nevertheless, this combination is far from being as simple as it seems. A deep and structured risk analysis is required to ensure a smooth implementation of lean 4.0 system within a company. The purpose of this paper is to discuss I4.0, lean manufacturing and Lean 4.0 risks and opportunities through a systematic literature review, and to study the different barriers to its implementation through the modelling of a case study using sequence diagram and SWOT analysis. Its contribution consists of being a reflection paper to the companies aiming to develop their digital maturity by exploiting Lean 4.0 tools. [ABSTRACT FROM AUTHOR]

Underestimation or overestimating activities' duration in construction projects leads to many problems like lack of funding or procrastination. However, estimating duration accurately for construction projects is difficult because of many factors like identified and unidentified negative risks. This paper tried to estimate realistic durations of steel framing activities in a high-rise building using a combination of Artificial Neural Networks (ANNs) and fuzzy logic algorithms with data from five similar projects. Initial planned durations and the top five pure risks are the inputs, and estimated actual durations are the outputs. The data from the first four projects was for training, and the fifth one was for simulation. Then, the estimated expected actual durations under uncertainties are compared to the actual durations of the project. The results showed that about 75% of the fifth project's activities had errors of less than three days compared to the actual durations. [ABSTRACT FROM AUTHOR]

The energy sector is an exciting area of research and study due to the global demand for oil and gas, improving and optimizing related costs and risks, essentially maximizing the business output. Unplanned corrective maintenance can lead to unscheduled downtime if not attended effectively and efficiently. Maintenance can restore system healthiness and avoid catastrophic failures. This paper explores the prioritization of unplanned work orders (WO) for corrective maintenance using an Artificial Neural Network. A case study from Oil and Gas in Oman was investigated to check the actual practice. The paper proposes a new approach to prioritize unplanned maintenance work orders, considering a classification of three correlated features: failure severity, asset criticality, and reliability. The proposed method shows the needs of such correlated of these features. The Artificial Neural Network-based multi-layer perceptrons method is applied; 82.7% of work orders being tested from the case study shows the low probability of less than 50% on initial priority. The investigation reveals the effectiveness of the suggested method to be applied to get more priority insights. We recommend industrial practitioners to use the approach that supports prioritization of recourses and scheduling activities better, saving cost and avoiding system functional failures. [ABSTRACT FROM AUTHOR]

Stack Overflow is a popular Q&A platform for developers to find solutions to programming problems. However, due to the varying quality of user-generated answers, there is a need for ways to help users find high-quality answers. While Stack Overflow's community-based approach can be effective, important technical aspects of the answer need to be captured, and users' comments might contain doubts regarding these aspects. In this paper, we showed the feasibility of using a machine learning model to identify doubts and conducted data analysis. We found that highly reputed users tend to raise more doubts; most answers have doubt in the first comment, and many answers have unsolved doubt in the last comment; high-score and low-score answers are equally likely to contain doubts in comments. Our classifier and findings can provide users with a new perspective on determining answers' helpfulness and allow expert users to easily locate doubts to address. [ABSTRACT FROM AUTHOR]

The rapid growth of social media platforms has led to an easier circulation of information around the world. There are a growing number of articles proposing various interventions to attempt to curb the spread of misinformation. This systematic literature review investigates the published research on misinformation interventions on social media platforms and provides an overview of existing literature on this subject by analyzing 346 papers included in major academic databases. We identify three categories of research, namely behavioral, modeling, and technical research, and classify various approaches proposed in behavioral research into three groups based on the research perspective, namely information, users, and official channels. Recommendations and guidance for future research are finally provided. [ABSTRACT FROM AUTHOR]

Hemodialysis patients frequently require ambulance transport to the hospital for dialysis. Some patients require urgent dialysis (UD) within 24 hours of transport to hospital to avoid morbidity and mortality. UD is not available in all hospitals; therefore, predicting patients who need UD prior to hospital transport can help paramedics with destination planning. In this paper, we developed machine learning models for paramedics to predict whether a patient needs UD based on patient characteristics available at the time of ambulance transport. This paper presented a study based on ambulance data collected in Halifax, Canada. Given that relatively few patients need UD, a class imbalance problem is addressed by up-sampling methods and prediction models are developed using multiple machine learning methods. The achieved prediction scores are F1-score=0.76, sensitivity=0.76, and specificity=0.97, confirming that models can predict UD with limited patient characteristics. [ABSTRACT FROM AUTHOR]

This paper is to develop a performance prediction model of an integrated system that combines a solar heat pump and an air heat pump based on the DNN (Deep Neural Network) model. In this paper, we describe the overall procedure for building a DNN model to predict the performance of an integrated system, including the data collection method, data set construction, and DNN model structure. To validate the reliability of the performance prediction model based on the DNN model, the mean square error of the coefficient of variation CV(RMSE) proposed by the American Society of Heating, Refrigeration and Air Conditioning Engineers Guideline 14 was used. The RMSE between the prediction result of the CV DNN model and the output variable was calculated as 5%. Therefore, the reliability of the performance prediction model based on the DNN model was verified, and the performance prediction accuracy was similar to the energy simulation model. [ABSTRACT FROM AUTHOR]

This paper aims to explore the main challenges facing the pharmaceutical industry today, as well as the potential opportunities offered by emerging Industry 4.0 technologies that can help address these challenges. To this end, a systematic literature review (SLR) based on a search with the keywords "Pharma" and "Industry 4.0" for identification, selection, and evaluation of the published research in this area, was performed. Thus, following this approach, two main questions were attempted to be answered: (i) "What is the As-Is situation that characterize the today's pharmaceutical production"; and, (ii) "How technological perspective presents itself as a possible To-Be situation by the use of I4.0 concepts". The key contribution of this research lays in the exploration of different obstacles in the current way how pharma is operating and what concepts from Industry 4.0 are in place to overcome them. The conclusion shows that the special situation in pharma due to its strong regulation by external authorities needs special ways of process design. [ABSTRACT FROM AUTHOR]

The increasing global population drives up food demand which means crop production needs to increase. To achieve that, fertilizer is used. Many industries, including the fertilizer industry, are feeling the impact of COVID 19. This paper presents the implementation of cost leadership strategy in Indonesia fertilizer company, the effect of COVID 19 on the fertilizer industry, and how the strategy is used to achieve competitiveness. This paper also discusses the impact of the implementation of cost leadership strategy to the company's financial performance. Based on the result, the five components of the cost leadership strategy can be fulfilled by the company. However, for the component that consumers have the power to bargain the price is not in accordance with the fertilizer industry. This is generally due to the fact that fertilizer prices in Indonesia depend on and affect world fertilizer prices. The impact of the implementation of the cost leadership strategy is carrying out effectiveness such as operational costs, managing inventory, and the use of raw materials. [ABSTRACT FROM AUTHOR]

The stator and rotor pole structure and power inverters have significant influence on electromagnetic torque performances and efficiency performances. Many papers have investigated on those design parameters on motor result [1][2][3] [4]. Inductance and flux linkage will be influenced by rotor and stator pole combination and magnetic circuit. In this paper, a SRM 12/10 will be modified from 12/8 with the same outer diameter stator and stack length. The paper figures out some stator and rotor pole arc to get maximum average torque and minimum ripple torque. For commercial switched reluctance motor, control method is single voltage or current pulse in higher speed not chopping current. The DC link voltage of power converter of SRM is limited, it is difficult to achieve constant torque in wider range of speed because the magnetic circuit is saturated condition, and the phase current cannot rise reference value. To improving average torque at high speed, increasing voltage or rotor poles or winding phases are possible. [ABSTRACT FROM AUTHOR]

This paper proposes an approach beyond the current Large Language Models (LLMs) milestones. To achieve optimal results, LLMs require careful fine-tuning. Using techniques such as prompt engineering and In Context Language (ICL), it is possible to provide LLMs with the necessary guidance to perform specific tasks with incredible accuracy and relevance. Deep Neural Networks have limited capabilities in intelligent behavior (i.e., in understanding the meaning of its input). Hence, the scientific community started to question more heavily in recent years whether Deep Learning alone could get us closer to Artificial General Intelligence (AGI). The authors of this paper believe that the training and adaptive behavior challenges were a big part of the problem and that symbolic techniques did not advance simultaneously as Deep Neural Networks. Hence, revisiting and renewing the symbolic techniques and combining them with Deep Learning models like the LLMs can bridge the gap between neural techniques and symbolic knowledge representation. Our proposed system has been shown to significantly improve the performance of LLMs in terms of hallucination rate while providing flexibility of semantic input processing to symbolic systems. [ABSTRACT FROM AUTHOR]

Digital transformation technologies can enhance work processes and redesign the employees’ tasks but could have a huge impact on the employee work deliverables and ultimately the goals and objectives of the organization. The rapid growth in cloud-based applications, real-time customer interactions and feedback, and data analytics reports are forcing organizations to redefine the business processes, organizational structures, and human resource utilization to gain a competitive advantage by incorporating and implementing the new digital technologies (Korn, 2018). Although any organization that would like to succeed in implementing a digital transformation strategy needs to focus on multiple disciplines and principles within the organization. Equally, an organization could potentially achieve its goals by re-engineering business processes and by looking at technologies that underpin and drive organizational advancements in their market segment but could be negatively impacted by the lack of skilled employees (Almgren and Skobelev, 2020). However, these depend on two elements, namely the choice of digital technology and financial capabilities with the ability to use it for value creation. This paper provides some insight into a potential conceptual framework to ensure an organization succeeds by focusing on the critical importance of highly skilled and competent human resources. The article reviews and defines the potential relationship and influence between the leadership styles and the utilization of the human capital within the organization, with the notion that an organization must then understand and select the correct digital transformation technology for their industry and ensure alignment with the organizational goals and objectives. Additionally, it advocates an approach to defining and measuring the return on investment through cost analysis processes and to ensure the implementation of the correct digital strategy roadmap. Recent research findings indicate that the estimated failure ratio is between 66% to 84%. (Correani, and De Massis, et al, 2020). The inconsistency between the strategy formulation and implementation process is the main issue for failure which has a huge financial impact on the organization’s revenue, work deliverables, and products and services. As a result, future management processes must ensure that the elements are managed and adjusted to address any future economic and market challenges, trends, and new opportunities. Subsequently, such an approach creates the foundation and building blocks for success, and future growth for an organization, which could be achieved and ensured through a conceptual framework for the successful implementation of an organizational digital transformation that ensures all the elements of an organization are redesigned and changed through the digital transformation project. (Brown and Harvey, 2006). [ABSTRACT FROM AUTHOR]

Machine learning techniques inspired by the structure and function of the human brain are known as artificial neural networks (ANNs). Their popularity has grown in recent years due to their ability to learn and improve via training, allowing them to be used in a variety of applications. The ANN approach has the appealing advantage of providing an accurate representation of the process input/output data and due to this reason, their application in the extraction metallurgy field have increased over the years. This systematic review aims to investigate the application and efficacy of ANN for facilitating and upgrading mineralogical data processing in the extraction metallurgical field. In this study, 112 research papers, published over years, that discuss ANN application were retrieved and 49 research papers were systematically reviewed. Research trends, ANN algorithms models, and evaluation methods primarily discussed in the 49 papers were discussed. From the review, it was discovered that the main justification to the increase application of ANN in the extraction metallurgy field is due to the ore grade decline which makes extraction processes complex demanding a need for a predicting and optimising tool. The frequently used ANN algorithm was found to Levenberg- Marquardt algorithm (LMA) which was due to its ability to process a lot of data in a short period of time. It was further concluded that the applicability of ANN in metallurgical field diverse from processes optimisation using operational to equipment parameters with least error and high efficiency. [ABSTRACT FROM AUTHOR]

This paper explores the suitability of Artificial Neural Networks (ANNs) as an enabler of Design Automation in the turbomachinery industry. Specifically, the paper provides 1) a preliminary estimation of the effectiveness of ANNs to define values for design variables of reciprocating compressors (RC) and 2) a comparison of ANNs performance with traditional and more computationally demanding methods like CFD. A tailored ANN trained on a dataset composed by 350+ Baker Hughes' RC automatically assigns values to 8 geometrical variables belonging to multiple parts of the RC in order to satisfy two target conditions linked to their thermodynamic performance. The results highlight that the ANN-assigned parameters return an optimal solution for RC also when the target values do not belong to the training dataset. Their predictive capacity for RC thermodynamic performance, with respect to CFD, are comparable (i.e. less than 2% in terms of calculated absorbed power) and the approach enables a significant gain in terms of computational time (i.e. 2 minutes vs 10 hours). Future perspectives of this work may involve the integration of this tool in an advanced DA method to lead Design Engineers (DEs) during the whole design process. [ABSTRACT FROM AUTHOR]

Finite element (FE) simulations can be used both in the early product development phase to evaluate the performance of developed components as well as in later stages to verify the reliability of functions and components that would otherwise require a large number of physical prototype tests. This requires calibrated material cards that are capable of realistically representing the specific material behavior. The necessary material parameter identification process is usually time-consuming and resource-intensive, which is why the direct inverse method based on machine learning has recently become increasingly popular. Within the neural network (NN) the generated domain knowledge can be stored and retrieved within milliseconds, which is why this method is time and resource-efficient. This research paper describes advantages and potentials of the direct inverse method in the context of the product development process (PDP). Additionally, arising transformation opportunities of the PDP are discussed and an application scenario of the method is presented followed by possible linkage potentials with existing development methods such as shape optimization. [ABSTRACT FROM AUTHOR]

A bone abnormality is a medical condition which is caused by physical damage or diseases. There are many factors which can lead to various abnormalities. An irregularity in a bone is generally diagnosed by orthopedician and radiologists using x-ray images of the affected bone. Bone abnormalities affect more than a billion people in the world. With more than 30 million emergency visits annually, there is a lack of expertise. Although computer aided diagnosis is still very limited in the world. They are mostly confined to research projects and there's been no real world applications. For the past few years there has been a great leap in the development of Artificial Intelligence which now makes it possible to implement and test deep learning models in the medical field. One can get an appropriate amount of data for these implementations. MURA is a database that was made available by Stanford University for testing purposes in a target of achieving a best model for detecting bone abnormalities. Our paper mainly focuses on the advancement in medical imaging technologies targeting the diagnosis at the level of experts for improving health care access. We used python, as a programming tool, and fastai, to process images and implement the model for abnormality detection. The proposed model follows a feed forward network resulting in a good accuracy rate. [ABSTRACT FROM AUTHOR]

This paper describes the use of the Microsoft Kinect for building three dimensional maps for use in teleoperation. Though these maps are being used for teleoperation in this paper, these map building techniques could also be applied in localization for navigation or robot path planning. The Kinect is a relatively new depth sensor that has become popular in the field of robotics, often replacing significantly more expensive systems like tilting laser range finders and stereoscopic vision systems. Two main sources of error in the map making are investigated, random and systematic error from the depth sensor and uncertainty in the navigation solution of the vehicle. Systematic and random error in the Kinect has previously been described in the literature [1] and this paper looks at how these errors affect the users ability to do mapping and offers some practical solutions. The teleoperation system design around this map making process is presented, and it builds on existing work [2] using octrees as the storage for the maps. [ABSTRACT FROM PUBLISHER]

We are into the technology era which means we have to utilize the maximum power of computational engineering to improve the manufacturing goals of a company. In this paper we have proposed a theory which will discuss how we can achieve it by using the Time forecasting and Artificial Neural Network for predicting the possible outcomes in Production and sales. This will help the industry to be ready for all possibilities. As we assume that in the manufacturing industry Manpower is the key factor and if we predict the quantity of the production required to be produced for upcoming months then the manufacturing company will be ready with the raw material and manpower which means the accurate planning will increase the profitability of the company. This paper is the combination of ARIMA Model using time forecasting and artificial neural network algorithms to analyze annually the manufacturing process of a company using the three major domains of procurement, marketing and sales. This paper will focus on the technique of data forecasting by using the previous information about raw material, purchase and production. We will achieve this by combining two modules together to show the output and accuracy both modules are compared. We have proposed the ARIMA module for time forecasting with the combination of artificial neural network algorithms which will be used to make the possible predictions based on available data. [ABSTRACT FROM AUTHOR]

Consumable materials in the electricity industry are the important materials needed to meet consumer demand for both new electrical installations and power addition. Stock-out on consumable materials often occur in the company used in this research case study. The purpose of this paper is to analyze and determine the safety stock, reorder point, and lot size policies to manage and control consumable materials in the case study. The forecasting method used in this research is ARIMA and Artificial Neural Network (ANN), as well as two types of lot-sizing rules, Economic Order Quantity (EOQ) and Periodic Order Quantity (POQ). Based on last year's demand data for new electrical installations and power addition, the ANN forecasting method is shown to be more appropriate because it has a smaller error than the ARIMA method. Twenty consumable materials were analyzed in this paper. Thirteen materials need to use the POQ lot-sizing rule, while the remaining seven need to use the EOQ rule correctly. The result shows that a change in the lot size rules used for each material, cause an increase in the service level. [ABSTRACT FROM AUTHOR]

This paper presents electricity forecasting by integration of Nested Long Short Term Memory (NLSTM) as the another type of LSTM, Attention Mechanism (AM) as the technique for Neural Network and optimized by Cuckoo Search Optimizer (CSO) as the metaheuristic. It is important because of the electricity forecasting issue. Electricity is the crucial energy, due to the many sectors which is require it. Estimation of electricity consumption can enhance the effectiveness of generation and supply. The shortage and outage of electricity is the thing that should be avoided through the electricity forecasting. In order to create better forecasting result, the forecasting by NLSTM-AM will be combined. NLSTM-AM has several parameters which influence the network performance. Those parameters will be optimized by CSO. To discover the performance of this proposed method, the RMSE of this method will be compared by other algorithms. In this paper there are two datasets which will be used, those are Taiwan electricity consumption and Turkey electricity consumption. It is proved that NLSTM-AM-CSO has the smallest RMSE rather than other algorithms. [ABSTRACT FROM AUTHOR]

The aim of the research presented in this paper is to propose a conceptual model and architecture of a service-oriented scalable framework, ensuring the implementation and verification of methods and algorithms for the integration, management, analysis, and visualization of biomedical data and the implementation of scientific research for the needs of precision medicine. The system architecture for big biomedical data analytics and discovering useful knowledge from data consists of the following components: biomedical data sources, data storage, data integration and preprocessing, real-time data flow, stream processing, analytical data storage, data modeling and analysis, and results visualization. A feed-forward artificial neural network is designed for data analysis, and during the training process, the input data is divided into training data and test data. The training error and its distribution over the weights of the neurons in the network are determined. A reduced set of statistical records related to cardiovascular disease analysis has been used as experimental data. The original database contains 76 attributes, and 14 of them have been used for the study. In addition, the data is split in a ratio of 0.8 to 0.2. The first 80% of the data was used to train the neural network and the remaining 20% to test the trained network. The calculated accuracy increases with increasing epochs and is higher for the training data and lower for the validation test data. Thus, the trained model can be saved, and loaded on another system, as well as available for review of the weight values. The trained model is applied in the system to calculate new input parameters that were not used either in training or validation. [ABSTRACT FROM AUTHOR]

Developing and maintaining fair AI is increasingly in demand when unintended ethical issues contaminate the benefits of AI and cause negative implications for individuals and society. Organizations are challenged by simultaneously managing the divergent needs derived from the instrumental and humanistic goals of employing AI. In responding to the challenge, this paper draws on the paradox theory from a sociotechnical lens to first explore the contradictory organizational needs salient in the lifecycle of AI-powered systems. Moreover, we intend to unfold the responding process of the company to illuminate the role of social agents and technical artefacts in the process of managing paradoxical needs. To achieve the intention of the study, we conduct an in-depth case study on an AI-powered talent recruitment system deployed in an IT company. This study will contribute to research and practice regarding how organizational use of digital technologies generates positive ethical implications for individuals and society. [ABSTRACT FROM AUTHOR]

The disruptive nature of digitalization and the complexity and ambiguity of their technical properties require many new skills from employees today. Recent research emphasizes that the employees' digital mindset plays an essential role in digital transformation by leveraging employee engagement. This paper aims to advance the understanding of how the behavioral outcomes of digital mindset, which encompasses interpersonal interaction, focus of attention, enthusiasm for development, perspective on setbacks, and construal of effort, can be positively influenced during digital initiatives. We develop a novel research model integrating two literature streams: information technology and mindset. We conceptually link back to the behavioral outcomes of digital mindset by looking at the influence of IT governance mechanisms as potential antecedents. Our model explains how IT governance mechanisms influence the behavioral outcomes of digital mindset and helps future researchers by providing propositions on the impact of IT governance mechanisms toward more employee engagement. [ABSTRACT FROM AUTHOR]

The modeling and analysis of information flow from various sources (e.g., analyst reports, news, and social media), and their impact on assets and investment decisionmaking, have drawn lots of attention. In this paper, we propose a new knowledge inference design framework that provides concrete prescriptions for developing systems capable of supporting knowledge-based investment decision-making. Our framework design incorporates the advantages of both knowledge graphs and symbolic reasoning engines through the concept of a dual system. On the other hand, it overcomes the weaknesses of traditional expert systems, saving time in the knowledge input process, reducing the introduction of errors, and achieving more comprehensive knowledge coverage to obtain better predictive performance. Moreover, our proposed design artifacts are of significant importance in addressing the issues of causality and interpretability in the literature. [ABSTRACT FROM AUTHOR]

Striving for innovation and advancement, a phenomenon can be observed wherein organizations are progressively incorporating their 'ordinary' employees into the innovation process, capitalizing on their creativity, expertise, and knowledge to foster novel ideas. Such integration mandates formalized yet flexible processes to offer a common ground for both employees as idea contributors and managers as decisionmakers, enabling control and governance. Despite this, a conspicuous knowledge gap exists within the realm of employee-driven innovation (EDI) concerning the design of EDI processes. In this paper, we present the outcomes of an action design research project conducted with a medium-sized organization, focusing on formalizing and designing an EDI process with decision points through three iterative cycles. This research contributes fourteen meta-requirements and eleven design principles for EDI process design, thereby expanding the theoretical (prescriptive) knowledge base. Additionally, the results offer practical implications, enabling organizations to adopt the EDI process accordingly. [ABSTRACT FROM AUTHOR]

Object detection using convolutional neural networks (CNNs) has achieved high performance and achieved state-of-the-art results with natural images. Compared to natural images, medical images present several challenges for lesion detection. First, the sizes of lesions vary tremendously, from several millimeters to several centimeters. Scale variations significantly affect lesion detection accuracy, especially for the detection of small lesions. Moreover, the effective extraction of temporal and spatial features from multi-phase CT images is also an important issue. In this paper, we propose a group-based deep layer aggregation method with multiphase attention for liver lesion detection in multi-phase CT images. The method, which is called MSPA-DLA++, is a backbone feature extraction network for anchor-free liver lesion detection in multi-phase CT images that addresses scale variations and extracts hidden features from such images. The effectiveness of the proposed method is demonstrated on public datasets (LiTS2017) and our private multiphase dataset. The results of the experiments show that MSPA-DLA++ can improve upon the performance of state-of-the-art networks by approximately 3.7%. [ABSTRACT FROM AUTHOR]

General practitioners are supposed to be better diagnostics to detect patients with serious diseases earlier, and conduct early interventions and appropriate referrals of patients. However, in the current general practice, primary general practitioners lack sufficient clinical experiences, and the correct rate of general disease diagnosis is low. To assist general practitioners in diagnosis, this paper proposes a multi-label hierarchical classification method based on graph neural network, which integrates medical knowledge and electronic health record (EHR) data to build a disease prediction model. The experimental results based on data consist of 231,783 visits from EHR show that the proposed model outperforms all baseline models in the general disease prediction task with a top-3 recall of 0.865. The interpretable results of the model can effectively help clinicians understand the basis of the model's decision-making. [ABSTRACT FROM AUTHOR]

In this paper, we address the related tasks of medication extraction, event classification, and context classification from clinical text. The data for the tasks were obtained from the National Natural Language Processing (NLP) Clinical Challenges (n2c2) Track 1. We developed a named entity recognition (NER) model based on BioClinicalBERT and applied a dictionary-based fuzzy matching mechanism to identify the medication mentions in clinical notes. We developed a unified model architecture for event classification and context classification. The model used two pre-trained models--BioClinicalBERT and RoBERTa to predict the class, separately. Additionally, we applied an ensemble mechanism to combine the predictions of BioClinicalBERT and RoBERTa. For event classification, our best model achieved 0.926 micro-averaged F1-score, 5% higher than the baseline model. The shared task released the data in different stages during the evaluation phase. Our system consistently ranked among the top 10 for Releases 1 and 2. [ABSTRACT FROM AUTHOR]

This paper addresses the task of answering consumer health questions about medications. To better understand the challenge and needs in terms of methods and resources, we first introduce a gold standard corpus for Medication Question Answering created using real consumer questions. The gold standard1 consists of six hundred and seventy-four question-answer pairs with annotations of the question focus and type and the answer source. We first present the manual annotation and answering process. In the second part of this paper, we test the performance of recurrent and convolutional neural networks in question type identification and focus recognition. Finally, we discuss the research insights from both the dataset creation process and our experiments. This study provides new resources and experiments on answering consumers’ medication questions and discusses the limitations and directions for future research efforts. [ABSTRACT FROM AUTHOR]

Effective implementation of Municipal Solid Waste Management (MSWM) strategies remains an issue in many developing countries. To analyse the effectiveness of the strategies, it is vital to understand the techniques employed to implement such strategies. Hence, this paper aims to analyse the techniques in implementing MSWM strategies in Sri Lanka. Single case study on Moratuwa municipality, which is considered as a critical case, was conducted. The study used a mixed approach; predominantly qualitative. Using purposive sampling method, semi-structured interviews were carried among 19 residents, one from each urban division of Moratuwa Municipal Council (MC), 03 MC officers, and 02 waste collectors. Additionally, 75 questionnaires were collected from residents. Code-based content analysis and quantitative statistical methods were used to analyse the interviews and questionnaires respectively. The community and MC have adopted various techniques in implementing MSWM strategies. Nevertheless, the community is not aware of some of the techniques enacted by MC. Strong communication between the MC officers and community, and between the different municipalities have been recommended for better management of MSW. The study has concluded by proposing some MSWM techniques that can be utilised by all municipalities in Sri Lanka as well as in other developing countries that face similar MSWM problems. [ABSTRACT FROM AUTHOR]

This paper presents an earth-friendly design of trash bin during the COVID-19 pandemic and new normal. Medical masks are widely used to protect and prevent people from getting infected by the coronavirus (SARS-CoV-2). This situation also increases the medical masks waste that is not disposed of properly. A survey was conducted with 100 respondents that consisted of both males and females above 17 years old in Indonesia to perceive the public trash bin's needs. SPSS software was used to analyze these survey data to confirm the validity and reliability of the data. Also, the study of product planning and development and anthropology were used to design an ergonomic product. Moreover, this paper aims to design an eco-friendly trash bin that can manage the medical mask waste, maintain hygiene, and be used for the public during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Tax evasion refers to an entity indulging in illegal activities to avoid paying their actual tax liability. A tax return statement is a periodic report comprising information about income, expenditure, etc. One of the most basic tax evasion methods is failing to file tax returns or delay filing tax return statements. The taxpayers who do not file their returns, or fail to do so within the stipulated period are called tax return defaulters. As a result, the Government has to bear the financial losses due to a taxpayer defaulting, which varies for each taxpayer. Therefore, while designing any statistical model to predict potential return defaulters, we have to consider the real financial loss associated with the misclassification of each individual. This paper proposes a framework for an example-dependent cost-sensitive stacking classifier that uses cost-insensitive classifiers as base generalizers to make predictions on the input space. These predictions are used to train an example-dependent cost-sensitive meta generalizer. Based on the meta-generalizer choice, we propose four variant models used to predict potential return defaulters for the upcoming tax-filing period. These models have been developed for the Commercial Taxes Department, Government of Telangana, India. Applying our proposed variant models to GST data, we observe a significant increase in savings compared to conventional classifiers. Additionally, we develop an empirical study showing that our approach is more adept at identifying potential tax return defaulters than existing example-dependent cost-sensitive classification algorithms. [ABSTRACT FROM AUTHOR]

This paper reports the development of a software suite to be accessed in future with any General Packet Radio Service (GPRS) or High Speed Packet Access (HSPA) enabled mobile phone or Personal Digital Assistant (PDA) for the extraction and analysis of disease-related features from the photograph of paper based ECG records. In India and other developing countries, the cheaper paper based ECG machines are prevalently used. In rural areas of these countries cardiac diseases are still the major silent killers due to the acute dearth of qualified cardiologists. One way of addressing this problem is Tele-medicine which necessitates an intelligent cardiac parameter extraction algorithm. In our bid to address this requirement, an algorithm is developed with the help of few image processing techniques. Initially, the background noises i.e. the gridlines are removed by thresholding technique. Applying the Sauvola method for adaptive image binarization and subsequent morphological operations to get pure ECG signature on white background, this algorithm intelligently applies Bresenham's line drawing algorithm to join the disjoined ECG signature where required. Then thinning has been done for extraction of digital time-plane data and then Discrete Wavelet Transform (DWT) and water reservoir based pattern recognition technique are subsequently used to delineate other important time-plane features for ECG interpretation. [ABSTRACT FROM PUBLISHER]

Intensive Care Unit (ICU) is a special department of any hospital for the critical patients. Premature discharge from ICU is a common incident due to shortage of clinical resources and economic problems. These discharged patients often return to ICU with more critical situation and risk. Thus the objective of this paper is to propose a model, comprising 18 health related variables to predict readmission after discharge and help doctors in taking decision. This paper also examined the performance of three different data mining techniques: Naïve Bayes (probabilistic), decision tree and neural network. The results showed that the proposed model provide the best result in case of artificial neural network with the prediction accuracy of 95.005, AUC of 0.874 and sensitivity of 0.950. The prediction model is trained and evaluated by using almost 11000 data from the MIMIC-III dataset. The skewness of the dataset is also considered while proposing the model. [ABSTRACT FROM AUTHOR]

The three-phase fault detection and classification can be done by using radial basis function neural network with wavelet transform can be proposed in this paper. The Discrete Wavelet Transform (DWT) is used in implementation of wavelet approach and the radial basis function neural network (RBFNN) will be utilized to detect and classify different types of faults. This paper mainly focuses on identifying the faults and classification by obtaining detailed co-officiants of different types of faults. The RBFNN is used to overcome the drawback of wavelet transform for detecting and classification of faults. In this paper the concept of wavelet transforms and RBFNN with power system network is verified with MATLAB Simulink. The simulation results are obtained from wavelet transform technique and radial basis function neural network (RBFNN) for three phase fault detection and classification. [ABSTRACT FROM AUTHOR]

This research paper has the main objective to study internal and external assessment proposed by Porter. Data collection was conducted by identifying the strengths, weaknesses, opportunities and threats of the company. Then perform weighting of internal and external factors for the company. The data is then processed using IFE (Internal Factor Assessment) and EFE (External Factor Assessment). Based on the result of the study, it was found that the company of testing and calibration service in Indonesia implementation of Internal and external assessment in cyber defense paltform calculation of the IFE Matrix and EFE Matrix values, the conclusion of the analysis is that the IFE Matrix value is 2.35 and EFE Matrix value is 2.15, when mapped to the Internal and External Matrix, quadrant V (stability zone) will be obtained. Quadrant V means that it is best controlled with an out-and-out strategy. The strategies commonly used are market penetration and product development strategies. [ABSTRACT FROM AUTHOR]

Pandemic situation forced all of people to work remotely. People depend on information technology to be able to work remotely. Telecommunication industries which an industry that contributes to the success of implementation information technology on remote working, have an increase in terms of demand due to the pandemic. AS result telecommunication industries must align their strategic implementation to their strategic formulation to enable survive the pandemic conditions. This paper will look and analyze how telecommunications industry in Indonesia refine their strategic implementation to their strategic formulation to answer the pandemic challenge. It was found that telecommunications industry that aligning their strategic implementation to their strategic formula, have a significant effect on their revenue on pandemic and post-pandemic era. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the change of page number and image processing methods for discrimination between behaviors in reading contents and taking notes for contents on the display. First, scene images for contents can be recorded by a sunglasses-type camera. Next, a few features can be extracted based on color information from these images and the discrimination procedure can be executed by the discrimination analysis and the classification method by using multi-layered neural networks. Finally, we evaluate the discrimination precision and discuss the relations among page transition, behaviors by students and their notes. This electronic document is a “live” template. The various components of your paper (title, text, heads, etc.) are already defined on the style sheet, as illustrated by the portions given in this document. [ABSTRACT FROM PUBLISHER]

This paper investigates the use of ANNs to model human behaviour in design by comparing the predictive capability of ANNs and engineering students. Function structure models of 15 products are used as input for prediction. The type of information provided varied between topology and vocabulary. Analysis of prediction accuracy showed that ANNs perform comparably to students. However, students are more precise with their predictions. Finally, limitations and future work are discussed, with research questions presented for subsequent research. [ABSTRACT FROM AUTHOR]

Citizens in large and modern cities heavily rely on smart and efficient public transportation as an alternative to private cars. Public transportation options are expected to be efficient, consistent, and reliable. For example, users of public buses should be able to use their smart phones to reserve and plan their trip at any time. They should also be able to track in real time their routes and any possible delays or issues. Bus adherence to their schedule in public transportation can be modeled as an NP-hard problem. This is due to the many unpredictable factors that can impact such adherence. In this paper, we used deep neural network and regression models to predict bus adherence to scheduled times. We selected San Antonio Transit system as a problem domain and used a dataset containing a snapshot of the adherence of VIA buses from February 2019. We focused on analyzing the significant routes in the dataset and explored the percentage of buses were on time in these routes. Results revealed better performance of neural network models as compared to regression models. [ABSTRACT FROM AUTHOR]