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South African pulp and paper mills have encountered frequent breakdowns of centrifugal pumps, resulting in considerable production losses. A case study was conducted to determine the factors contributing to these failures and devise ways to enhance pump reliability. Data were collected through questionnaires and operational records. The study revealed that inadequate maintenance, misalignment of pump components, lack of lubrication, improper baseplate installation, subpar sealing arrangements, inadequate design of the pumping system, and foreign materials entering the system were the primary causes of pump failure. To improve reliability, this study suggests employing machine learning methods to select the optimal maintenance strategy and provide internal maintenance personnel with training in condition monitoring systems. This will enable both pulp and paper mills and pump manufacturers to identify blind spots and improve the dependability of centrifugal pumps. [ABSTRACT FROM AUTHOR]

Assuring product quality has been always a main concern of a production process. PT IGP Internasional Sleman is a company that produces Printed Tissue Paper. This company's defect tolerance limit is 2%. Based on early observations, the defect rate reaches 5.2%. The Eight Disciplines (8D) method was used to solve the problem. First, the team was built to engage all related divisions in the project. Then, Failure Mode and Effects Analysis (FMEA) was carried out to sort the problems. Root Cause Analysis using Fishbone diagram was also used to find the causes. Proposed improvements to reduce defects have been implemented. There are adding Rotogravure machine operators, conducting supervision/inspection by production leader, and providing socialization related to the importance of product quality and production. The implementation has successfully reduced the defects to fulfill the company's standard. [ABSTRACT FROM AUTHOR]

Attendance monitoring is an essential task for any organization and traditional methods such as pen and paper or electronic card systems have their limitations with advancements. The proposed model is a real time face presence monitoring system using Raspberry Pi. The system captures image of individuals using camera and processes the images using deep learning algorithms to detect faces. These faces are then matched against a pre-existing database of known individuals. Door interlock controls add an extra layer of security, ensuring that only authorized person have access to restricted areas. The system provides a user-friendly interface to view everyone's attendance records and generate attendance management reports. The proposed system provides a reliable and accurate solutions for attendance tracking and can be used in various organizations. [ABSTRACT FROM AUTHOR]

A growing field in India that can assist farmers in many ways is the identification of plant diseases. Plant disease reduces the mass production in agriculture. Every time due to heavy loss in agriculture, farmers face many challenges to raise up their needs. This study analyzes various diseases that affect various plants due to the following criteria like environmental condition, types of plant formed from Monocot and Dicot family, crop rotation and Ph value of the soil. The existing system analyzed the diseases based on one of the criteria. Although the strategy is to be followed to identify plant diseases at the early stage with various aspects so that the production in agriculture could be increased. The majority of machine learning algorithms already exist and may be improved upon to yield more accuracy. Xanthomonas wilt, Black and Yellow Sigatoka, Bunchy Top Virus, Black Sigatoka, Fusarium wilt, Cassava Bacterial Blight (CBB), Cassava Brown Steak Disease (CBSD), Cassava Green Mite (CGM), and Cassava Mosaic Disease (CMD), Yellow vein mosaic virus, and Powdery Mildew are a few of the diseases of the plants grown on crop rotation which are the subject of study of this paper. Plant illnesses are classified according to disease in the many open-source datasets that are accessible. For deeper understanding, we have provided a list of references. Here, we've examined the approaches that are now in use for automatically detecting various diseases of plant leaves. We present an approach for accurately identifying early signs of plant diseases from a variety of perspectives. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) and Machine Learning (ML) transforming the Insurance industry by improving efficiency, reducing costs, and providing a better customer experience. As these technologies continue to evolve, more innovation can be expected in the Insurance industry. A Bibliographic analysis is conducted for scientific mapping based on 1,084 SCOPUS-indexed publications between the year 2000-2022 using VOSviewer Application. The Analysis was conducted based on Publications by year, Source, Author, Affiliation, Country, Type, Subject Area and Funding Sponsors. The research found the result, the year 2022 had the highest publication of 203, through documents per year by the source of Lecture Notes In Computer Science Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics at 43 publications, Bauder, R.A. author is the maximum contributor, Harvard Medical School has been the major affiliate, United States of America, has the maximum 341 publications, Conference paper has the majority participation at 412 documents at 38 percent and Keywords "Artificial intelligence", Machine Learning" and "Insurance" has the highest occurrence. The maximum number of publications inthe field of computer science at 28 percent. Overall, this bibliographic analysis provides a comprehensive overview of the current state of research in AI and Machine Learning in the Insurance industry and highlights the potential for further innovation and development in this field. [ABSTRACT FROM AUTHOR]

We present SciA11y, a system that renders inaccessible scientific paper PDFs into HTML. SciA11y uses machine learning models to extract and understand the content of scientific PDFs, and reorganizes the resulting paper components into a form that better supports skimming and scanning for blind and low vision (BLV) readers. SciA11y adds navigation features such as tagged headings, a table of contents, and bidirectional links between inline citations and references, which allow readers to resolve citations without losing their context. A set of 1.5 million open access papers are processed and available at https://scia11y.org/. This system is a first step in addressing scientific PDF accessibility, and may significantly improve the experience of paper reading for BLV users. [ABSTRACT FROM AUTHOR]

Floods in Malaysia are described as flash floods and monsoon floods by the Department of Irrigation and Drainage (DID). Forecasting is a specialized form of predictive analysis used to predict future trends or behavior from existing data. Weather forecasting predicts the weather in the future from current data circumstances. As flood forecasting models become more accurate, however, their capacity to accurately predict flooding decreased as the forecast continues. Therefore, to understand the different techniques used to forecast flood levels, a systematic review of the literature was conducted. This paper's main objective is to examine the most common variable used to forecast floods, utilizing the systematic review technique. From the main focus, we can identify the research questions, such as the most commonly used prediction method and its accuracy. In the end, two of the most common variables used for flood forecasting are rainfall and water level. This study's data can help others forecast floods using standard variables that yield the best accuracy possible. [ABSTRACT FROM AUTHOR]

Finding the lineage of a research topic is crucial for understanding the prior state of the art and advancing scientific displacement. The deluge of scholarly articles makes it difficult to locate the most relevant prior work and causes researchers to spend a considerable amount of time building up their literature list. Citations play a significant role in discovering relevant literature. However, not all citations are created equal. A majority of the citations that a paper receives are for providing contextual, and background information to the citing papers and are not central to the theme of those papers. However, some papers are pivotal to the citing paper and inspire or stem up the research in the citing paper. Hence the nature of citation the former receives from the latter is significant. In this work in progress paper, we discuss our preliminary idea towards establishing a lineage for a given research via identifying significant citations. We hypothesize that such an automated system can facilitate relevant literature discovery and help identify knowledge flow for at least a certain category of papers. The distal goal of this work is to identify the real impact of research work or a facility beyond direct citation counts. [ABSTRACT FROM AUTHOR]

In most nations, lung cancer (LC) is the main cause to mortality cases occurring due to tumors to males and females. Lung nodules (masses) are recognized through digital technologies support precise diagnosis using medical imaging techniques such as CT-scan, X-rays and MRI. As the nodules are normally connected to the blood vessels, identification of lung nodules is a difficult task. Several studies illustrate that diagnosing or detecting such cases earlier is regarded the best successful method to counter this disease. Many systems have been developed that automatically identify lung tumors by using magnetic resonance spectroscopy and pattern recognition methods. Despite the encouraging consistency of the classification, none of these schemes have set their course in clinical practice, which also concerns the lack of clarity concerning the process of taking decisions. In this paper, we present a systematic mapping study on lung tumors recognition. After four sorting methods, we obtained 375 relevant studies in total. After applying four filters manually, 48 papers as primary studies related to the main topic are selected as listed in Appendix (A). The selected papers classified them with respect to several facets. The results provide an overview of the existing relevant studies in reported in the literature, highlight focus areas and research gaps. [ABSTRACT FROM AUTHOR]

This study investigated the trends in quality 4.0 and what they mean for various industries. Scopus was the main database used in the systematic literature review. Only papers written in English that directly addressed the study's research objectives and were published between 2017 and 2023 were included in the analysis. The number of papers on quality 4.0 has increased exponentially over the years. The majority of papers came from the TQM Journal, followed by the International Journal of Quality Research and Sustainability Switzerland. In our investigation into what quality 4.0 means for industries, we used a sample of 14 papers. We used qualitative data analysis known as thematic coding, and through inductive thematic coding, we were able to identify 99 codes that were associated with quality 4.0. The study found that quality 4.0 is a broad term encompassing nearly all aspects of quality management, including big data, digital quality management, connectivity, cybernetics, managing disruptions, simulation, and more. According to the study, quality 4.0 is unlikely to change the traditional quality management strategy; rather, it will make things smarter, faster, and more intelligent. The study's findings were consistent with previous research and had both reliability and validity. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has brought about major health crises globally and the persistent nature of long COVID has put a significant strain on hospitals as a result of scarce healthcare resources. Heart problems are discovered to be the most prevalent among persons who have recovered from COVID-19 among several long-lasting effects (Gupta et al., 2021). There are so many cases of what is generally occurring with post-COVID patients. This work tries to find out the proper machine learning methods to predict the post-COVID complexities which can provide the highest accuracy. The true objective of this endeavor is to conduct additional machine learning-based research in this area of medical science and to develop an accurate machine-learning model. In the case of COVID patients, there are a lot of outcomes in a post-COVID condition. Proper problem definition, data acquisition, Model selection, evaluation of training and testing models, etc., are also significant obstacles to constructing a decent prediction model. There were proposed machine learning models according to previous research which model is combined with multiple models. This research might contribute to making further approaches to predicting the complexities of other diseases like COVID-19. [ABSTRACT FROM AUTHOR]

The author has been researching this topic over the past 18 months and noticed significant activity in this area with numerous Reinforcement Learning (RL) tunnel and geomechanics related papers being published beginning from around 2020. This paper reports on the author's current knowledge of this topic from a selected literature review to running an experimental RL tunnel model and developing a simple model with aid of ChatGPT 'code interpreter' to aid in generating the Python code. RL is an area of Machine Learning (ML) concerned with how agents (say the tunnel engineer) might take an action in an environment (the tunnel) to obtain a reward and not a penalty. It focuses on finding a balance between exploration and exploitation. The RL model progress can be easily monitored over time. The amount of computational computer hardware resources required to analyse a reasonably sized model and within a practical timeframe can, in many cases, be significant (days and even weeks). Two of the examples referenced are known to have taken several days on desktop computers with or without the aid of the Graphical Processing Unit (GPU). An overview of RL is also given including the underlying concept of neural networks and RL. [ABSTRACT FROM AUTHOR]

Recently, the volume of scientific literature has grown rapidly raising an imminent question about its storage and organization. Many research papers are often available only through the websites of the relevant scientific journals. It is an essential problem when different classification codes are used in order to organize these papers or when specific categorization in a certain scientific field is missing. This leads to unnecessary complications in the researchers' aims who want to quickly and easily find literature on a specific topic among the large amount of scientific publications. Simultaneously, the research interest related to the mechanisms of natural language processing is growing because much of the information they work with is unstructured and in the form of plain text. In order to improve and automate the process of organizing and classifying scientific papers we propose an approach based on the technology for natural language processing. This applies the methods of supervised machine learning and two specific algorithms for text categorization - Support Vector Machines (SVM) and Naive Bayes (NB). The proposed approach classifies the scientific literature according to its contents. To successfully execute our scientific research, we used over 200 papers, published in the last four years in the journal "Izvestiya", which is issued by the University of Economics - Varna. The articles explore different topic areas and are written in English. The experiments were conducted with the software product RapidMiner. [ABSTRACT FROM AUTHOR]

There has been a substantial growth of technology and it has evolved a lot, and with the evolving technology credit cards seem to be replacing cash payments. There is no second thought about credit cards getting used on a large scale but this extensive use brings a huge number of fraudulent uses of credit cards. In this paper, discussions on different technologies and methods that are used to detect and prevent credit card fraud have been presented. In the paper discussion on the statistics of the credit card frauds detected using machine learning and deep learning has been presented. In this paper, we have presented the survey on Machine learning techniques and analyzed their accuracy and speed, also we have discussed the methods that are used in detecting credit card fraud using deep learning and artificial intelligence. Furthermore, in this paper, various types of datasets are explained in detail. Also, the paper highlights the challenges and possible solutions to detect fraud. [ABSTRACT FROM AUTHOR]

Textbook reading over many decades for higher education students is commonly reported at 30% or less. The objective of this paper is to integrate an existing static textbook with modern web tools to increase student interaction. Using a web-based platform called Perusall (perusall.com), students in a graduate course in fluid mechanics annotated a traditional textbook and journal papers. Students' questions about or comments on the readings were awarded a fraction of the total grade for the course. Features used to engage students in textbook readings will be discussed as well as example responses, scoring, inter-student discussions, faculty intervention, and end of semester survey. The quantity and quality of comments were graded using machine-learning algorithms built into the platform. Each comment was auto-graded on a 0, 1, or 2-point scale, the machine scoring agree very well with the professional judgment of the instructor. The spacing of comments across each assignment was accounted for also, which helps insure reading of the entire assignment. [ABSTRACT FROM AUTHOR]

In this world of ever-increasing texts, chats and literature users nowadays are posting countless comments on various social media platforms, news websites, and discussion forums. While some comments bring the positive side of the matter, whileother remarks are detrimental or combative in nature. Manually monitoring of such a large volume of comments and chats is a tedious and a time taking task. Therefore, various machine learning based techniques are employed to cater the task of detecting the levels of toxicity in the comments. We gathered information from 20 primary research papers that were found to be relevant in the field of toxic comment classification. We explored all the papers thoroughly taking into consideration the publishing date andconferences in which they were published. We noted the data set used, performance criteria, machine learning techniques employed and other important features such as diversity of toxicity classes and comparative report stated in them. We concluded our work with a detailed overview of the existing issues and a suggestion list of future topics that could be explored and implemented to make the system more accurate in classifying the toxic comments. [ABSTRACT FROM AUTHOR]

To increase the safety and security of the house, a smart home safety system is developed, taking into many practical considerations. This home safety kit is formed using the Internet of Things (IoT) which includes face detection and face recognition with Artificial intelligence & Machine learning by training the dataset. The technical methodology is developed and implemented in a Smart Home Safety System Kit, which will work with the essential support of a Cloud-based application. The same data is stored with a local storage system for quick access, and at the same time, data are stored Cloud for critical situations. WIFI transmitter and receiver, and microcontroller (NODE MCU) are used with Arduino, and many drivers are installed to take care of each purpose. The Node MCU is an available open-source with low-cost IOT Platform. It initially consisted of firmware running on Espressif Systems hardware based on the ESP12 module and ESP8266 WiFi SoC. [ABSTRACT FROM AUTHOR]

As societies rely increasingly on computers for critical functions, the importance of cybersecurity becomes ever more paramount. Even in recent months there have been attacks that halted oil production, disrupted online learning at the height of COVID, and put medical records at risk at prominent hospitals. This constant threat of privacy leaks and infrastructure disruption has led to an increase in the adoption of artificial intelligence (AI) techniques, mainly machine learning (ML), in state-of-the-art cybersecurity approaches. Oftentimes, these techniques are borrowed from other disciplines without context and devoid of the depth of understanding as to why such techniques are best suited to solve the problem at hand. This is largely due to the fact that in many ways cybersecurity curricula have failed to keep up with advances in cybersecurity research and integrating AI and ML into cybersecurity curricula is extremely difficult. To address this gap, we propose a new methodology to integrate AI and ML techniques into cybersecurity education curricula. Our methodology consists of four components: i) Analysis of Literature which aims to understand the prevalence of AI and ML in cybersecurity research, ii) Analysis of Cybersecurity Curriculum that intends to determine the materials already present in the curriculum and the possible intersection points in the curricula for the new AI material, iii) Design of Adaptable Modules that aims to design highly adaptable modules that can be directly used by cybersecurity educators where new AI material can naturally supplement/substitute for concepts or material already present in the cybersecurity curriculum, and iv) Curriculum Level Evaluation that aims to evaluate the effectiveness of the proposed methodology from both student and instructor perspectives. In this paper, we focus on the first component of our methodology - Analysis of Literature and systematically analyze over 5000 papers that were published in the top cybersecurity conferences during the last five years. Our results clearly indicate that more than 78% of the cybersecurity papers mention AI terminology. To determine the prevalence of the use of AI, we randomly selected 300 papers and performed a thorough analysis. Our results show that more than 19% of the papers implement ML techniques. These findings suggest that AI and ML techniques should be considered for future integration into cybersecurity curriculum to better align with advancements in the field. [ABSTRACT FROM AUTHOR]

In the current era, one of the key challenges in utilizing the Holt-Winters Method of forecasting is the accurate selection of smoothing coefficients. To address this issue, researchers have explored an optimization approach that aims to minimize forecasting errors, such as Mean Squared Errors (MSE) or Mean Absolute Deviation (MAD). This paper presents a novel methodology that employs a Genetic Algorithm (GA) to optimize the forecasting error by determining the optimal smoothing coefficients for the Holt-Winters Method. The objective value of the optimization problem is the Mean Square Error (MSE), which serves as a measure of the accuracy of the forecast. To evaluate the effectiveness of the proposed approach, actual test cases based on rice stock commercial commodity in the Philippines during the COVID-19 pandemic were utilized. The paper examines different variants of the Holt-Winters Method and assesses their suitability for capturing the characteristics of the rice stock data. The findings indicate that an additive seasonal effect is more appropriate for modeling the seasonal patterns observed in the rice stock data. Furthermore, the performance of the proposed GA-based approach is compared to other forecasting models to ascertain its efficacy. The results demonstrate promising outcomes, suggesting that the GA-based optimization approach for determining the smoothing coefficients in the Holt-Winters Method improves the accuracy of rice stock forecasting during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The easy accessibility of social media to everyone generates serious problems. Misleading news affect the mental health of peoples. False news can easily be created and propagated using online platform by using an Un-anonymous account. it is required to control the spread of fake news on social media. In this paper we used several linguistic features for fake news classification along with machine learning model. The linguistic features used are number of characters, number of words, noun-count, and number of articles. This study provides the heuristic solution by using both the news text and Linguistic features of text for better news classification. LIAR dataset is used for experiments. Our method outperforms the existing method. [ABSTRACT FROM AUTHOR]

Conversational agents (CAs), which communicate naturally with humans, are being developed and employed for a variety of tasks. Interactions between humans and CAs induce affect, which is vital to the adoption and performance of CAs. Yet, there is a lack of cumulative understanding of existing research on affect in human-CA interaction. Motivated thus, this article presents a systematic review of empirical IS and HCI studies on such affect, its antecedents and consequences. Besides conducting descriptive analysis of the studies, we also divide them into two broad categories - emotion-related, and those related to other (more persistent) affective responses. We present organizing frameworks for both categories, which complement each other. Through the review and frameworks, we contribute towards attaining a holistic understanding of extant research on human-CA interaction, identifying gaps in prior knowledge, and outlining future research directions. Last, we describe our plan for extending this work to gain additional insights. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to cover the many different pieces of research that have come out in the last six years. By classifying the topics covered in these papers, and then analyzing the contents and suggestions made by them, this paper offers a conglomeration of the information stored to help others plan future research in the field. Analysis of how certain proposed solutions work, and how their results are achieved is also done with some real-world statistics. Using all this information, examples of future research into smart mobility are given based on the information parsed in the making of this paper. [ABSTRACT FROM AUTHOR]

This paper aims to review several related papers on Quality Management and Industry 4.0 in the pharmaceutical industry environment to achieve the benefits required future and give a better vision of the extant regulatory and technical barriers for realizing it. Quality Management and industry 4.0 require efforts to better understand a GMP (Good Manufacturing Practices) pharmaceutical industry situation. Product quality in the pharmaceutical industry is a fundamental thing that must be achieved as a fulfillment of regulatory requirements. GMP for the Pharmaceutical Industry is a minimum standard of compliance for the pharmaceutical quality system requirement that must be designed, applied, managed, and maintained properly so that the main objective is achieved where the production process so that medicinal products always have robust quality in fulfilling efficacy and patient safety. Industry 4.0 in the pharmaceutical industry (which is popular with the term Pharma 4.0) is challenging to implement within the GMP 'high-regulated industry' environment. It will have consequences to moving transition into it step by step in a delicate careful slide. The internet of things (IoT), artificial intelligence (AI), robotics, machine learning, cyber-physical system, and advanced computing as a character of industry 4.0 will dramatically change the landscape of manufacturing including the quality system inside. The research results presented in this paper show an increasing trend towards research that focuses on the slices of 'quality management', 'pharmaceutical quality system' and 'industry 4.0'. Further research needs to be done to create a pharma 4.0 quality system implementation model. [ABSTRACT FROM AUTHOR]

This review paper delves into the significant advancements and explores future directions in the application of digital twin technology within machining processes, offering a comprehensive synthesis of current research, technological breakthroughs, and real-world applications. By systematically examining the integration of digital twins in enhancing operational efficiency, predictive maintenance, quality control, and customization in machining, the paper identifies key benefits such as improved productivity, reduced downtime, and enhanced product quality. Furthermore, it highlights existing challenges, including the complexity of implementation, data management issues, and the need for substantial initial investment. Drawing on a wide range of academic and industry sources, the review identifies gaps in current research and proposes potential interdisciplinary applications and technological innovations that could overcome these hurdles. The paper concludes with a forward-looking perspective, suggesting areas for further research and the development of standards to fully realize the transformative potential of digital twins in machining, thereby setting a new paradigm for future manufacturing practices. [ABSTRACT FROM AUTHOR]

Diabetes is one of the leading global health emergencies of the 21st century. The threatening side effects proves it to be a dangerous epidemic. This paper proposes a model to solve the problem in the existing system by comparing various data mining techniques in order to cluster and classify them. The paper studies a huge sample of diabetic patients common to diabetes and cardiovascular disorders. The method adopted is to cluster the data into three prior categories of high, medium and low clusters. Once clustered, optimum algorithmic technique is implemented to classify the data into mild and severe fragments in order to determine the chances of occurrence of a heart disorder due to the impact of diabetes. The aim is to implement an algorithm that can classify the data collected in order to infer whether or not a diabetic patient is prone to a heart disorder or not. [ABSTRACT FROM AUTHOR]

Though in their infancy, mixed reality systems have proven to aid humans in mission critical tasks, once performed manually and requiring diverse skilled manpower. Industry 4.0 initiatives have seen mixed reality systems successfully adopted by many aerospace manufacturing environments, however aerospace maintenance and sustainment has been slower to adopt such initiatives, for various reasons. The paper reports about a mixed reality-based aircraft maintenance task that has been performed and compared with a non-mixed reality-based manual task. The data was collected using computer vision and machine learning techniques unobtrusively, generating both qualitative and quantitative measures, and a usability metric was derived. Furthermore, feature selection techniques are employed to determine the accuracy of the machine learning (ML) based usability assessment model. Supervised learning methods are employed for unstructured physiological data, and a usability metric for mixed-reality systems has been assessed based on dynamic features. The preliminary results are encouraging and lay the foundation for deriving data-driven models to predict usability of mixed reality systems when used in complex aircraft maintenance tasks. [ABSTRACT FROM AUTHOR]

The traditional software space (1.0) has seen more than fifty years of creation, testing, and delivery of deterministic software, but this tradition is being disrupted by machine learning (2.0). However, ML and traditional software are considerably different, and the nascent ML industry uses unique workflows and toolsets for both life cycles. This "one foot in each raft" scenario forces companies to support duplicate resources which are essentially doing the same thing. This paper proposes an answer to the research question: Can software 2.0 quality assurance be performed effectively using the same process as 1.0? A systematic literature review was performed and process documents from a machine learning company reviewed. 132 papers were selected and refined to 24. ML process documents supported what is an industry standard ML life cycle. While the literature review shows a gap relative to holistic QA solutions for ML products, process documents showed the answer to the research question is...perhaps, since it was determined that the typical ML life cycle can be mapped to the standard software 1.0 life cycle. This paper proposes using that mapping to extend an existing 1.0 QA process architecture, the Quality Assurance Machine (QAM), to effectively manage both 1.0 and 2.0 QA. [ABSTRACT FROM AUTHOR]

"Online learning is not the next big thing; it is the new big thing." "eLearning is changing. And we will see new models, new technologies, and designs emerge. So, let us drop the "e" – or at least give it a new and wider definition." These quotes influenced us to build this paper. During this unprecedented time, we all are using online resources. Our paper will enhance the teaching experience of the students. Especially school-going kids, it is the duty of teacher, influencer, and faculty to enhance the teaching experience so that, one can learn at their ease without any hustle. Our paper aims at building a website and android application (in the coming days) which will identify the individual words of any sentences entered. It will parse the string entered, remove the unwanted characters, and pass the remaining string to our AI model. As a result, the AI model will return the parts of speech the individual word belongs to. We have always seen students struggling to find that a given word is either a noun, or an adverb, or a preposition, and so on. We have searched the web but could not find any relevant websites doing the same task. There is a gigantic search engine on which we can search individual words but again, for sentences, people struggle. This may sound very simple for AI/ML enthusiasts but building a model and deploying that on a website and android application is not a cup of tea. Tech giants like Google, Facebook, and Microsoft are building different AI models and deploying them successfully on various platforms like Website, Android Application, iOS. However, we have done it successfully. [ABSTRACT FROM AUTHOR]

The study thoroughly and systematically reviewed the extent literature on knowledge management research to examine 1) how knowledge management is defined by researchers, 2) trends in knowledge management research, 3) what theories and methodologies have been applied in literature on knowledge management research, 4) what constructs and variables has been assessed in the knowledge management literature, 5) what is the future of knowledge management research in relation to industry 4.0. Peer-reviewed articles mainly from AIS library (journal and conference papers) and other sources published between 2012 and 2018 are content analyzed. The results of the study provide valuable information to knowledge management researchers by identifying gaps and disparities in the knowledge management literature. The study provides several important suggestions and directions for future research. [ABSTRACT FROM AUTHOR]

This paper presents a case study describing the design of SwageXR, an augmented reality (AR) application that gamifies a tubing assembly exercise. The aim of this paper is to contribute to the field of extended reality (XR) applications by providing a case study of a novel design process based on an existing framework by Gilardi et al. Design needs and requirements were gathered using a contextual design approach and qualitative research through interviews with training course instructors as well as a workshop involving them. The contextual design approach involved the PI taking part in the training course as one of the trainees to observe and build understanding of the taught practical content and the context in which the learning happened. This was followed by semi-structured interviews with 4 training instructors, with results indicating preferred course content to digitise, early concepts for gamification, and the storyline of the gamified content with respect to existing training. Using the data and experience gained from the contextual design approach and interviews, a paper prototype of the gamified AR application was developed, and feedback was obtained from the company technical director. The paper prototype and feedback were used as a basis for developing a 3D prototype in VR using ShapesXR. This led to a virtual reality (VR) workshop involving the technical director and training manager of the company where the 3D prototype was demonstrated and discussed. The workshop resulted in the flow of the overall application being refined including the storyline, gamification, training, and challenges. The design process aims to facilitate the development of a coded working prototype for SwageXR, with gamification elements such as score, time-based challenges and leaderboards. An initial experience playtest with course trainees will then be possible to gain insight on engagement levels and trainee attitudes about the initial game experience. The main outcome of this research is a design approach that tries to address the gap outlined by Krauß et al., the approach makes use of rehearsal techniques and virtual reality to provide context for AR application design. [ABSTRACT FROM AUTHOR]

Recommendation systems (RS) suggest the right item to the right user. It predicts the user's rating to an item and based on this rating RS provides the suggestion to users. In today's world many online applications are already using the Recommendation system that provides a recommendation for a particular item like books, movies, music etc. in an automated fashion. This paper proposed a system that helps to find the best suitable Medicineaccording to diseasesby using library "recommenderlab" in R. This study proposed a system that gives the best medicine available according to the user rating available in database. User makes their decision according to their recommendation provides by the proposed system for finding best suitable medicine from available database [ABSTRACT FROM AUTHOR]

The global population is estimated to increase by 2.6 billion by 2050, in which 70% of the world's population will be staying in the cities. Countries will have to introduce solutions to traffic congestion, pollution, and security. South Africa is not immune to the global challenges and the rise in population. The purpose of this study is to evaluate the effectiveness of Smart Transport Systems in developing countries. The study used a qualitative approach, and scoping research methodology technology to review the papers published between 2015-2021, which were specifically concentrated on South African smart cities and transport. Articles relating to smart cities and transport in South Africa were intensively reviewed and 250 papers were selected. It was found that despite the challenges which South Africa as a country is facing, relating to the socio-economic, its initiatives are gradually developing especially in different metropolitan municipalities which are leading to very successful projects in both the public and the private sector. South Africa does have smart transport infrastructure in place which gives a good kick start and development of smart transport. However, good monitoring, evaluation, and planning of the current infrastructure are of great importance. [ABSTRACT FROM AUTHOR]

The notion of risk exists in every aspect of the business, as it cannot be eliminated but rather reduced to an attainable level through the utilization of effective risk management techniques. For the insurance industry in particular, risk is traded and transferred to the insurance providers as the company offers a shield from the exposure to risk consequences and the likelihood of loss, therefore, escalating the risk from the insured entity to the insurer for a given premium. This research is a development on a previously published paper by the author which had focused on the same issue but with the utilization of binary regression. The paper now proposes a modern model to risk classification which will be used for property lines insurance. The significance of the research lies in the fact that the process of risk prediction can be extremely complex since there are many parameters to keep in mind. In addition, accurate premium pricing can be difficult to estimate given the unpredictability of the risks occurring to the covered property. The data will be categorized into "A", "B", "C", and "D" using multinomial regression followed by a pricing model. The proposed model will be validated via data collected from surveyed properties of a UAE based insurance company. The model is expected to serve as a tool that helps provide better estimates of risk, premiums, and precise pricing. [ABSTRACT FROM AUTHOR]

Existing evidence indicates that Free/Libre Open-Source Software (FLOSS) ecosystems offer extensive learning opportunities. Community members actively participate in various activities, both during their interactions with peers and while utilizing these environments. Given that FLOSS repositories contain valuable data on participant interactions and activities, our study focuses on analyzing knowledge exchange and interactions within emails to track learning activities across different phases of the learning process, with a focus on the first phase (Initiation). In this paper, we leverage Natural Language Processing (NLP) and Machine Learning (ML) techniques within a process mining framework. Specifically, we employ NLP techniques to analyze the contents of emails and messages exchanged in these FLOSS repositories to generate event logs for the purpose of modeling learning patterns. Subsequently, we construct corresponding event logs, which serve as input to Disco, the process mining tool, for learning process discovery in these environments. The output comprises visual workflow nets that we interpret as representations of learning activity traces within FLOSS, capturing their sequential occurrences. To enhance the understanding of these models, we incorporate additional statistical details for contextualization and description. This approach enables a nuanced exploration of learning dynamics within FLOSS environments, emphasizing the role of NLP and ML in uncovering valuable insights on how FLOSS participants acquire and exchange knowledge. [ABSTRACT FROM AUTHOR]

Machine learning (ML) based approaches provide efficient solutions successfully applied to different domains. In Software Engineering (SE) domain, ML is improving and automating various development activities, such as requirement classification, refactoring, defects prediction, and effort estimation. We investigated whether and how ML techniques currently support software development tasks and improve the way practitioners do their job. To achieve this objective, we performed a literature review and snowballing, obtaining a set of 209 articles. As a result, we present an organized approach among the first ten areas of SWEBOK, which guides possible ML solutions used in the tasks of the SE. [ABSTRACT FROM AUTHOR]

Many types of machines widely used in Industrial application process. The main objective is to recognize faults failure based on motor speed and vibration signal. To find fault machine with the use of vibration analysis in turning equipment and induction motor. Every defect can associated with cost and performance of the machine. Due to these associated risk such as cost, time, efficiency early detection, isolation and failure prediction is important. Using machine learning algorithm to predict the fault failure rate of machine easily. Signal level are monitored with vibration analysis. Depends upon the vibration signal faults are identified and classified. In this paper we discuss to classify the bearing the fault using different data mining techniques. To predict the imbalance condition, fault in bearing, and broken rotor bars faults are predicted using machine learning algorithm. [ABSTRACT FROM AUTHOR]

Most real-world classifications problem provides an uneven, skewed dataset. Imbalanced datasets are a key issue in machine learning. The challenge for any machine learning algorithm to learn from imbalanced dataset is correctly classifying cases from the minority class. Imbalanced dataset leads machine learning algorithms to misclassify cases from the minority class which is undesirable for real world classification applications and also degrades the performance of algorithm. Glass identification dataset is one of the datasets that serve as a example of skewed imbalance real world dataset. The majority of research on glass identificationtasks ignores the data outlier and class imbalance found in this dataset. Class imbalance, data outlier, and insignificant featuresare only a few of the factors that contribute to the poor performance of distance-based machine learning methods such as kNN. Thus, this paper explores the performance of one of the most simple and efficient machine learning classifier kNN. The authors performed interquartile range (IQR) thresholding to eliminate data outliers, ReliefF ranking to find significant features and SMOTE to cope with the class imbalance occurring in the dataset. The Inverse distance weighted kNN is utilized for classification in the improved dataset. The results revealed that using the strategies mentioned improves the performance of the kNN algorithm while also providing accuracy that is better and more dependable than much of the research done in this dataset. In this paper the authorsfound the highest accuracy of glass identification task is 78.923% with F-measure 0.791. [ABSTRACT FROM AUTHOR]

From the past few years spreading of the pests and diseases in plants have been increasing significantly. Paddy plants are the most important crop used in our country for the food, so it is most necessary to detect any disease of them within a short period of time for ensuring a proper and healthy growth of paddy plants. The process of manual disease detection requires labor and a large amount of time. Then utilizing the leaf images of plants for recognizing and classifying of diseases is the more focused research topic in the agriculture field. A survey on recognition and classification of paddy leaf diseases using image processing and machine learning techniques is presented in this paper based on the disease infected leaf images of paddy plants. Firstly the concept of various plant diseases and the standard process of plant disease detection are discussed in this paper. A study and survey on the totally 5 papers of work is carried out in detail by covering a work on leaf diseases of the paddy plants based on the certain criteria. Such criteria are different preprocessing techniques, various diseases/ classes, different segmentation methods, various classifiers and accuracy of employed techniques. The experimental results of these various techniques are compared and evaluated to design a best on detecting and classifying of paddy plant leaf diseases. [ABSTRACT FROM AUTHOR]

As the construction industry heads towards the Fourth Industrial Revolution, the gap widens between industry demands and fresh graduate capabilities. Much of this is owed to the traditional Construction Management (CM) pedagogy. Therefore, there is a pressing need for a paradigm shift in our approach towards CM education. As such, to meet both industry demands and student expectations, integrating game-based learning (GBL) can substantially enhance the learning experience for CM students in higher education. Although the body of knowledge recognises Game-Based Learning (GBL) as tools that facilitate active learning in engineering disciplines in general, their utilisation remains limited in construction management. Thus, the aim of this paper is to conduct a systematic literature review of past GBL studies in Construction Management via Scopus database with an attempt to classify these studies into three broad categories: Tabletop games, digital games, and computer-assisted games. First, this paper will explain the key features, strengths, and limitations of each category. Second, this paper will identify the trends in game characteristics, learning objectives, data collection methods, and game evaluation approaches. Finally, this paper will present a classification framework that summarises the findings to facilitate selecting effective GBL strategies for CM higher education. Thus, this study will provide a wide-lens view of the research trends, gaps, and future research recommendations in this field as well as present design parameters for effective GBL integration. [ABSTRACT FROM AUTHOR]

The paper discusses various failure modes associated to the links of bucket chains used in gold and tin mining in marine and alluvial environments. Failure control and the simulation of static and dynamic stresses developed in the operation of bucket ladder dredges using the Finite Element Method are also examined in detail. [ABSTRACT FROM AUTHOR]

Color sorting, control by touch or sound and ability to perform auto-sorting are characteristics of modern robotic arms and make them desirable in automatic lines. The paper discusses the implementation of sensor control in a robotic arm suitable for flexible manufacturing and emphasizes the benefits of such addition in terms of scalability. [ABSTRACT FROM AUTHOR]

Lightweight and stable robotic arms are essential devices in flexible manufacturing. The paper introduces a scalable robotic arm with 5 degrees of freedom controlled either by mouse or joystick and powered by serial bus servos and discusses its stability, kinematic characteristics and hardware and software implementation. The authors emphasize the benefit of 3D computer design in the mechanical development of scalable robotic arms. Such approach allows a quick and precise evaluation of distances between the joints with direct consequences in kinematic evaluations, interchangeability of bus servos required in scalability and overall an optimized design. [ABSTRACT FROM AUTHOR]

The paper emphasizes the benefits of employing magnetic fluids as lubricants in the development of hydrodynamic bearings in terms of carrying abilities. A brief discussion regarding the preparation of lubricants with unique magnetic properties is followed by analytical and numerical approaches for establishing the carrying ability of hydrodynamic bearings. The flow simulations, performed in identical geometrical and dynamic conditions, shows the increase in carrying ability and reduction of cavity in hydrodynamic bearing lubricated with magnetic fluids. In addition, the retention of the magnetic lubricant between the journal and bearing in the presence of a constant magnetic field permanently provides fluid friction for all flow regimes. [ABSTRACT FROM AUTHOR]

A common and growing issue in scientific research and industry is that of storing and sharing ever-increasing datasets. In this paper we document the development and applications of Baler - a Machine Learning based tool for tailored compression of data across multiple disciplines. [ABSTRACT FROM AUTHOR]

In the complex mosaic of the digital age, the tactical incorporation of artificial intelligence (AI) within knowledge management (KM) is revealed as a central business component of technology management. The current study aims to clarify the intersection between KM and AI in organizational contexts. Specifically, this paper represents a preliminary step to investigate the potential impacts of AI on KM research and practice. Building on a database we created from Scopus, we shine a spotlight on trends in pertinent peer-reviewed scientific articles published in the last decade (2013-2023) on the KMAI nexus. In addition, the paper presents an extended systematic analysis of literature, which synthesizes theoretical and empirical works conducted to date on this topic. Through a review of the available studies, we strive to shed light on effective KM frameworks and strategies in the era of AI. As extant research in the literature is largely theoretical, we propose to conduct empirical research on AI technologies in core KM processes such as acquisition, documentation, sharing, and application of knowledge. In addition, we recognize that the challenges and barriers to implementing AI in KM systems are not in focus and deserve to ignite further research. The anticipated contributions from such inquiries promise not only to augment the corpus of knowledge within the discipline, but also to furnish KM practitioners with the insights necessary for the crafting of efficacious systems. This research marks the advent of a transformative scholarly epoch, wherein the harmonious integration of KM and AI emerges as the bedrock of organizational ingenuity and strategic acumen. It distinguishes itself from prior works by pinpointing knowledge gaps in the synergy between disciplines and underscores the imperative for future research to bridge these lacunae. [ABSTRACT FROM AUTHOR]

The rapid advancements in Information Technology (IT) have profoundly impacted various aspects of human resource management, leading to the emergence of Electronic Human Resources (E-HR) as a transformative force in HR practices. This study investigates the future of IT and E-HR, examining emerging trends, innovative technologies, and their implications for the HR landscape. Drawing on a comprehensive review of literature and case studies, this paper explores key trends shaping the future of IT and E-HR, including artificial intelligence (AI), machine learning, data analytics, cloud computing, robotic process automation (RPA), and virtual reality (VR) technologies. It examines how these technologies are revolutionizing HR functions such as recruitment and selection, talent management, performance appraisal, learning and development, and employee engagement. Furthermore, the study delves into innovative applications of IT in E-HR, such as chatbots for HR assistance, predictive analytics for talent management, gamification for employee training, virtual reality simulations for onboarding, and blockchain technology for secure HR data management. It assesses the potential benefits, challenges, and ethical considerations associated with the adoption of these technologies in HR practices. Moreover, the paper explores the implications of the future of IT and E-HR for HR professionals, organizations, and the workforce at large. It discusses the evolving role of HR professionals as strategic partners and data-driven decision-makers, the impact of technology on job roles and skill requirements, and the importance of fostering a digital-ready workforce through upskilling and reskilling initiatives. Through a forward-looking analysis, this study offers insights into the future direction of IT and E-HR, highlighting opportunities for HR innovation, organizational transformation, and enhanced employee experiences. It concludes by discussing the strategic imperatives for organizations to embrace digital HR transformation and stay ahead in the dynamic landscape of HR management in the digital age. [ABSTRACT FROM AUTHOR]

Renewable energy has gained immense attention due to its potential to reduce the world's dependency on fossil fuels and mitigate climate change. As the energy harnessing rate from renewable sources depends on some external parameters like solar radiation, wind speed, direction, and turbulence, the generation rate in such sources comes with a high level of fluctuations. Fluctuations in their output can increase operating costs for the electricity system and be quite challenging for utility companies to always maintain a proper balance between the generation and usage of electricity. To reduce the operation cost and increase the reliability of the system, robust forecasting models are used to predict the generation rate and energy demand. This review paper offers a thorough examination of cutting-edge data-driven forecasting models utilized in forecasting renewable energy generation and demand. The paper organizes previous studies into five distinct groups based on prediction time frame: immediate, very short-term, short-term, medium-term, and long-term. It subsequently assesses the performance of various forecasting models, including three primary categories: time series, machine learning, and ensemble models, concerning predicting energy demand and generation rates across different time frames, using standard performance evaluation metrics. The findings indicate that ensemble models employing neural networks and support vector machines demonstrate notably higher accuracy rates in predicting energy demand and generation rates compared to the other models. [ABSTRACT FROM AUTHOR]

Lean thinking is an essential concept that has emerged in most organizations and industries. This is due to its proven outcomes in improving the process by removing waste and delivering value to the customer. Applying lean has also benefited the process by making it more sustainable by reducing the waste that negatively affects the three pillars of sustainability (Environment, Economy, and Society). Although lean has been established as a concept to improve the manufacturing process, it has extended to cover the maintenance process with reliance on established maintenance principles like Total Productive Maintenance (TPM) and existing lean methods. This paper aims to study and analyze a preventive maintenance (PM) process of a construction company in Qatar and implement lean maintenance methods with TPM to improve the process by minimizing lean waste with a focus on improving process sustainability. The methodology has been assessed through a real case study from a construction project of the company selected. The project equipment preventive maintenance process was analyzed, sustainability indicators were identified, and lean maintenance tools were implemented. The results have revealed that the process has been improved, and sustainability wastes have been minimized. [ABSTRACT FROM AUTHOR]

There is a growing interest in the concept of Industry 5.0 (I5.0), which supports a transition towards a more sustainable, resilient and human-oriented industrial paradigm. This paper focuses on the I5.0 sustainability pillar, which is crucial to optimize and support I5.0’s data-driven and circular supply chains. In order to pursue a Sustainable Supply Chain (SSC) and improve data quality management across it, it is first imperative to understand to what extent the existing SSC quality indicators are aligned with the opportunities of the digitalization era, and if they support the path to I5.0 sustainable practices. This paper aims to do a comprehensive study and analysis of relevant groups of SSC metrics and indicators. Through a broad literature review, SSC metrics and indicators are identified. Then, they are assessed to determine if they are suitable and relevant to assist decision makers in the challenges towards SSC 5.0. Results reveal that despite growing interest in the progress of data-driven SSC in recent years, there are still research gaps, mainly concerning the standardization of indicators, the simplification of models, and the use of consistent models for the treatment and conversion of proper data into relevant information. [ABSTRACT FROM AUTHOR]