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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]

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]

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]

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]

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]

This study conducts a comprehensive bibliometric examination to analyze Model-Based Customer Churn Research. Through Scopus, PRISMA, and VOSviewer. last two decades, academic publications and interest have increased. Empirical research, growth, and trends are examined in this paper. Evidence suggests churn prediction models using advanced data analytics, machine learning, and industry-specific methods. This shows scientific collaboration to predict industry churn. The study highlights "Expert Systems with Applications" and "ACM International Conference Proceedings Series," which spurred field dialogue. These publications provide cutting-edge research and expert opinions. Geographically, we study China, India, and the US. Global churn research matters. Bibliometric research shows model-based customer churns evolve. The resource can help academic and business researchers develop innovative client retention methods in different markets. By analyzing Model-Based Customer Churn the research studies, growth, and trends are examined. Evidence suggests churn prediction models use advanced data analytics, machine learning, and industry-specific methods. This shows scientific collaboration to predict industry churn. The study highlights "Expert Systems with Applications" and "ACM International Conference Proceedings Series," which spurred field dialogue. These publications provide cutting-edge research and expert opinions. Geographically, we study China, India, and the US. Global churn research matters. Bibliometric research shows that model-based customer churns evolve. The resource significantly assists academic and business researchers in developing innovative client retention methods in different markets. [ABSTRACT FROM AUTHOR]

In a research study, collected and processed data are needed to solve problems and prove hypotheses. However, datasets often contain missing or null values. One way to overcome the missing values problem is by using imputation techniques. The technique works by filling in the missing values with an estimated weight that has been analyzed to create a complete dataset. In the process, researchers usually found the data used for imputation to have unclear or inconsistent characteristics that may lead to bias. This issue can be addressed by implementing the Fuzzy C-Means (FCM) method to estimate the missing values and improve the data quality. However, estimating missing values using the FCM model produces predictive models with various parameters; hence, another approach to creating the best model with optimal parameters. Therefore, this underlies the need for an ensemble system combining different machine learning models to earn the best estimation result of missing values, including the fuzzy machine learning models. The ensemble system in this study uses majority voting and averaging, which can help boost accuracy without making the FCM system more complex. This research paper is born of the novelty of the combination of both designs through Ensemble FCM Model with Majority Voting and Averaging research topic. This topic in this study works to impute the missing values of Chronic Obstructive Pulmonary Disease (COPD) data in 2012-2017 from Cipto Mangunkusumo Hospital (RSCM) established the actual data. In addition, this study can help the hospital predict the exacerbation of COPD patients in the future. The random forest classification is used to create a prediction more trusted. As a result, this research paper compares the FCM model with and without the ensemble to prove the performance improvement. [ABSTRACT FROM AUTHOR]

This review paper thoroughly examines the prescriptive analytics approach to employee recruitment, focusing on the use of machine learning and mathematical programming to enhance the hiring process and revolutionize HR-related decision-making. It also draws upon valuable insights and practical recommendations from existing literature, providing guidance for organizations seeking to improve their recruitment planning and expansion strategies. Divided into two main sections, the study first explores techniques for extracting insights from the literature to evaluate the potential success of recruitment efforts. The second section presents a dependable mathematical model that comprehensively optimizes the hiring process, considering various organizational-level factors. Additionally, it investigates forecasted hiring success rates using machine learning, incorporating these predictions into a comprehensive recruitment optimization framework that addresses broader parameters and organizational goals. The mathematical programming model offers an effective solution for optimization, even when dealing with balanced demand and diversity requirements, maintaining high accuracy compared to actual recruiter selections. The paper concludes by highlighting limitations in current models for identifying transcription factor binding sites and suggests that variable-order Bayesian networks (VOBN) provide greater flexibility in addressing nucleotide dependencies. [ABSTRACT FROM AUTHOR]

The paper outlines the creation of a small-scale corpus intended for use in question pair similarity tasks. The dataset consists of World War II-themed sentences gathered from Reddit. The BERT language representation model was used to obtain the sentence vectors and narrow down the initial question pair pool. This was done by comparing the cosine distance between the sentences and selecting the pairs with a distance below an empirically chosen threshold. The selected sentence pairs were then subjected to a round of manual tagging to separate them into positive and negative examples by tagging them as either 1 (similar) or 0 (dissimilar). Additional sentence pairs were found by means of automatic keyword matching. This part of the process was based on relevant World War II-themed keywords (e.g. Germany, Churchill, atomic bomb) extracted from the manually tagged sentence pairs. The tagger also leveraged the NLTK framework for word synonym generation. The assumption was that semantically similar sentences would have multiple significant keywords in common, so sentences were tagged as similar if they shared at least one keyword pertaining to the relevant countries, nationalities and/or leaders (e.g. USSR), and at least one contextual keyword that was not a stopword (e.g. attack). The question pairs selected by means of automatic tagging were then subjected to a round of manual tagging, and positive matches were combined with the positive and negative pairs gathered in the previous steps to obtain the final corpus. The finished corpus comprises approximately 5600 positive and negative question pairs. [ABSTRACT FROM AUTHOR]

In textile design, patterns are traditionally printed on clothing, often comprising repetitive elements. Designers create these patterns, which are then assembled into a complete design. The use of Generative Adversarial Networks (GANs) offers a novel approach, enabling designers to draw inspiration for new ideas by generating textile patterns. Various GAN variants, with advantages in diverse fields, including fashion, have been proposed for this purpose. This paper explores the integration of deep learning, particularly Generative Adversarial Networks, to generate textile designs to provide ideas and facilitate the creative process of the designers. Leveraging Wasserstein GAN and Wasserstein GAN with Gradient Penalty, this study compares their performance in generating pleasant aesthetic results, taking into consideration simples conditionalization of the models. The GAN framework, consisting of a generator and discriminator, refines the image generation process through collaborative learning. A comparison between the models is done visually and by loss stability. This research also addresses the need of dedicated machine learning algorithms for textile design, providing insights into the intersection of technology and creativity in the textile industry. [ABSTRACT FROM AUTHOR]

This research explores the transformative potential of AI-based credit scoring and its profound impact on financial inclusion. The financial industry is experiencing a paradigm shift, with AI and machine learning at its core. Traditional credit scoring models have struggled to provide equitable access to credit, particularly for marginalized communities. AI-based credit scoring promises enhanced precision, faster decision-making, and broader credit access. However, it also raises ethical concerns and necessitates robust regulatory frameworks to ensure fairness, transparency, and data privacy. This paper not only highlights the critical role of regulatory oversight but also offers valuable insights for policymakers, financial institutions, and fintech companies aiming to harness AI's potential while addressing challenges to foster a more inclusive financial landscape. Keywords: AI-based credit scoring, Financial inclusion, Fintech companies, Inclusive financial [ABSTRACT FROM AUTHOR]

Reinforcement learning has emerged as a transformative paradigm in artificial intelligence, showcasing remarkable applications across diverse domains. Simultaneously, simulation has proven to be a crucial tool for constructing environments conducive to interactions with agents trained through reinforcement learning algorithms. This synergy opens avenues for refining policies within simulation models. While existing research has predominantly harnessed reinforcement learning in agent-based and discrete event simulation models, there is a noticeable gap in its application within the realm of system dynamics simulation. This simulation paradigm, widely employed in Industrial Engineering and Operations Management for decision-making processes, presents a unique set of challenges. This paper explores the integration of reinforcement learning techniques into system dynamics models, aiming to address the scarcity of applications in this context. By doing so, it seeks to extend the benefits of reinforcement learning to enhance decision-making processes in industrial and operational settings. The research delves into the nuances of policy exploration within the framework of system dynamics, providing insights into novel alternatives for optimizing and improving policies. Through this exploration, the paper contributes to the evolving landscape of reinforcement learning applications, particularly in the crucial domain of system dynamics simulation. [ABSTRACT FROM AUTHOR]

Rice is the staple food for Asia and a part of the Pacific, including Malaysia. However, the self-sufficiency ratio (SSR) for rice production in Malaysia has dropped from 69% in 2019 to 65% in 2021. For the continuous prosperity of Malaysia in food security, therefore this paper aims to investigate the statistically significant factors that affected the reduction of rice production based on the Cross Industry Standard Process for Data Mining (CRISP-DM) data science methodology. To pursue the principal objective of this paper, the annual rice production dataset period 1980-2019 corresponding to the atmospheric, climatic, and socio-economic factors under big data has been employed. Meanwhile, the regression-based predictive models employed in this paper are including multiple linear regression (MLR) and multiple nonlinear regression (MNLR) supervised machine learning models. The empirical analysis revealed that the MLR is superior rather than the MNLR in predicting rice production. Both supervised machine learning models consistently showed that the planted area and annual population are the statistically significant factors for the supervised machine learning models. In summary, this study is competent to support the advancing sustainability theme via the principal focus of poverty income revision. Specifically, the superior parsimonious predictive model resulting in this study is competent to beneficial for smallholder farmers in adopting plantation strategies for the future. Furthermore, this predictive model also could be beneficial to policymakers by providing early alarm insight about the national impact of recent atmospheric, climatic, and socio-economic trends on rice production. [ABSTRACT FROM AUTHOR]

This research paper explores the transformative potential of Internet of Things (IoT) technology in mitigating the longstanding issue of post-harvest losses within the agriculture sector. These losses, which encompass both quantitative and qualitative deterioration of food commodities from harvest to consumption, have posed persistent challenges, resulting in economic losses and food wastage. By delving into the current landscape of post-harvest losses and the application of IoT technology, the paper offers valuable insights into how IoT can be harnessed to reduce these losses effectively. It not only highlights the benefits and existing IoT solutions but also addresses the inherent challenges, providing recommendations for their resolution. Moreover, the research introduces a machine learning-based model, specifically Random Forest ML, to identify and prevent losses in tandem with IoT devices, empowering farmers with timely alert messages for informed decision-making, thus fostering a more sustainable and efficient agricultural ecosystem. [ABSTRACT FROM AUTHOR]

ASD is a common condition that affects 1 in 44 people, according to the Centers for Disease Control and Prevention (CDC). A lot of suffering occurs during this illness for both the children's parents. So early detection of disease is an immediate requirement. Many papers have been published with approaches involving image, video, emotions, face to face interaction, web browsing, social interaction and gaze and demographic studies. The use of eye-tracking technology, machine learning, and other diagnostic methods is currently used to assess early-onset ASD. Now, subjective criteria rather than objective ones are used to assess ASDs. There is evidence to support the idea that combining eye-tracking and machine learning could be a helpful tool in the early and accurate diagnosis of autism. This review paper intends to focus on all such research contributions and bring out a new methodology for early detection of ASD in childrens. The study reveals that video-based study has been effective with 92% accuracy. [ABSTRACT FROM AUTHOR]

To offer the patients' medical care and successful outcomes, a successful hospital management is required. Traditional hospital management systems, which typically rely on manual procedures, are unable to handle the vast amount of data that is generated in the field of health in an effective manner. The statistical management method for healthcare systems that we discuss in this paper is based on machine learning. A significant amount of hospital-related data, including patient records, medical procedures, staffing levels, and resource distribution, are evaluated using the methodology, which makes use of automatic learning capabilities. The paper's objective is to create predictive models that, through patient demand projection, resource optimization, and resource improvement, assist hospital management in making better decisions. To correctly forecast future patient loads, bed occupancy rates, and staffing requirements, analyze previous data to identify patterns and trends. These predictions can assist hospital administrators in efficiently allocating resources, cutting down on patient wait times, and raising patient satisfaction. Additionally, our approach gives hospital administrators dashboards and visualizations that provide crucial performance indicators, merging data-driven decision-making, so they can monitor and assess the effectiveness of their management strategies. Utilizing automatic learning algorithms ensures the adaptability and scalability of the hospital management system. [ABSTRACT FROM AUTHOR]

Copyright of Scientific Journal Systemy Wspomagania w Inzynierii Produkcji is the property of P.A. Nova S.A. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With burgeoning technologies, comes novel ideas that could potentially boost businesses and solve existing problems in the contemporary world. Tremendous advantages of the hybrid realm of the Internet of Things and Machine Learning can be rightly exploited for large benefits. One instance of day-to-day activities is the necessity to wait in long queues at the billing counters in shopping malls, which could be tiresome. Also, any strategy that has been made to improve sales in shopping malls is futile. This paper, hence, proposes a system that attempts to improve customer convenience as well as aims to maximize sales. An IoT-based approach using ESP32 and RFID modules is adopted, combined with Data Mining techniques to achieve the motive. Also, this paper describes how the billing data is transferred over Wi-Fi to different devices, the transaction of data between ESP32 and Firebase, and methodologies adopted to improve sales by recommending products to customers based on transactional history by creating a user interface. [ABSTRACT FROM AUTHOR]

People in Tamil Nadu have been more interested in history during the past years. When people see the old script inscribed on stone walls, potteries, copper plates, and Palm Manuscripts, history is more intriguing. But people have to learn old scripts in order to read these inscriptions, so not many people are capable of reading them. A ground breaking effort that educates the public about these ancient scripts and eventually sparks their interest in history is the use of deep learning algorithms for script recognition. Recognizing ancient Tamil characters is a difficult challenge for epigraphers because the language has evolved over the previous three thousand years with distinct characters set. Though there have been a few studies published on the recognition of ancient Tamil alphabets, there have been no papers on the recognition of ancient Tamil numbers. This project's main objective is to use convolution neural network (CNN) to recognize Tamil digits used between 9th to 12th century in Tamil inscriptions. In this paper work, the inscription photographs acquired from various Tamil temples are pre-processed. The color photographs were cropped, converted to grayscale and then to binary images during the pre-processing process. These pre-processed images are used as training and validation datasets for the model. [ABSTRACT FROM AUTHOR]

Managers are entitled to massive quantities of information in the digital era. Data analytics refers to databases that are vast, exceedingly diversified, and continuously changing, making it tough to alter them using standard tools and approaches. These data sets are expanding quickly, so it is necessary to investigate and implement ways to process and derive worth as well as familiarity from them. Governing members need to be capable of generating intelligent inferencesfrom a range of constantly evolving information sources, like data from social media, transaction data, and consumer connections. Data analysis, which uses sophisticated analytical techniques on the data, can be used to produce this value. This project attempts to study some of the numerous analytical approaches and tools that may be employed with metadata as well as the potential afforded by applying data analysis in diverse decision-making scenarios. WhatsApp has established itself as the most widely used and effective platform for interacting in recent times. It facilitates diverse interactions among people, covering a wide spectrum of topics. The wealth of information exchanged on WhatsApp contains immense value for cutting-edge technologies like machine learning. The crucial aspect for a machine learning model is to receive accurate and pertinent data, which is greatly influenced through the information provided to the model. This program seeks to thoroughly investigate the data derived from WhatsApp conversations. Regardless of the subject of discussion, our generated code can be leveraged to obtain deeper insights from the data. The tool's advantage resides in its use of simple Python modules like pandas, matplotlib, seaborn, and text analytics. These packages facilitate in constructing structures for data and generating various plots and graphs, which are applicable to large datasets. Consequently, the tool provides practicality and efficiency with little resource consumption. Recruitmentis a challenging procedure, where the initial task for recruiters is to sift through resumes. Today, many organizations prefer online employment application in comparing to paper resumes. This paper provides an effective Business Recommendation System that assist employers to pick the most suitable person for specified occupation title using information extraction and machine learning technologies. When applicants input their applications, then based on the firms needs for the job chances, the candidate's resume receive ranking. The grading isavailable for the employer to get most favored candidates. [ABSTRACT FROM AUTHOR]

Heart disease is one of the fatal human diseases that cause sudden death, and the ratio of deaths has been rapidly increasing globally in both developed and developing countries in recent days. As per the record of World Health Organization (WHO), approximately 18 million people face death every year as a result of heart disease. Coronary Artery Disease (CAD) is a type of illness in which the heart fails to supply adequate blood to all parts of the body for normal function. Heart disease and CADs have emerged as the most life-threatening problem which occurs not only in India but also worldwide, and this is the cause of the large number of deaths. Early detection and diagnosis of heart problems is critical for preventing death and saving patients' lives. The traditional method of predicting heart problems is ineffective. As a result, it is critical to accurately identify the problem at an early stage. In recent days Machine learning plays a vital role in identifying patients with heart disease. This paper discusses about the various techniques involved in interpreting ECG signal and its issues. Also, the various machine learning methods which are applied so for from the various paper for the classification of Heart problem is clearly narrated. [ABSTRACT FROM AUTHOR]

An innovative method for creating a music player based on emotion detection using machine learning is proposed in the abstract of this research paper. The system has the following modules: Image Capture using OpenCV, Face recognition using HAAR Cascade, Emotion recognition using FER algorithm, Music Database, and Music player. This project seeks to develop an intuitive music player that can choose songs in accordance with the user's emotional condition. The user is recognized by the Face recognition module after the user's face has been captured by the Image Capture module. The Emotion recognition module uses the Face Emotion Recognizer (FER) to detect the user's emotional state. Songs from the Music Database that match the user's mood are played by the Music player based on the emotion that was detected. This project aims to enhance the user experience of listening to music by creating a personalized playlist based on their emotional state. The proposed system is evaluated using several metrics such as accuracy, precision, andrecall. The results show that the system is capable of accurately detecting emotions and selecting appropriate songs from the Music Database. The proposed system can be extended to various applications, including gaming, marketing, and healthcare, where emotions play a crucial role in decision-making. In conclusion, this research paper presents a distinct and creative method for creating a music player based on emotion recognition through machine learning. [ABSTRACT FROM AUTHOR]

Online advertising has a big impact on whether your business succeeds or fails. Because of this, it is crucial to assess your advertisement's effectiveness before posting it online. Finding the Click-Through Rate (CTR) allows for this. Unfortunately, because you must gather user clicks before determining Click-Through Rate, this method is not environmentally friendly. In this situation, CTR prediction is helpful. For forecasting ad Click-Through Rate, user click data is a crucial source of information. Accurate Click-Through Rate prediction for contemporary e-advertising platforms is a challenging and crucial undertaking. Click-Through Rate prediction employs machine learning methods to determine how many times a potential consumer has clicked on an online ad. The more clicks an advertisement receives, the more successful it is. In this paper, we create a machine learning-based Click-Through Rate prediction model. Finding the Click-Through Rate allows for this. Unfortunately, because you must gather user clicks before determining Click-Through Rate, this method is not environmentally friendly. In this situation, CTR prediction is helpful. For forecasting ad Click-Through Rate, user click data is a crucial source of information. Accurate CTR prediction for contemporary e-advertising platforms is a challenging and crucial undertaking. Click-Through Rate prediction employs machine learning methods to determine how many times a potential consumer has clicked on an online ad. The more clicks an advertisement receives, the more successful it is. In this paper, we create a machine learning-based Click-Through Rate prediction model. The proposed study defines a model that produces accurate results with minimal use of computational resources. Three classification methods were used namely logistic regression, decision tree classifier and random forest classifier. Awasu dataset was used for analysis. The click data is generated over a 10-day period and sorted chronologically. This study answers the following question: Considering a user and the page they visit. What is the likelihood that they will click on a particular ad? The Random Forest classifier proved to be the best model with an accuracy score of 96%. [ABSTRACT FROM AUTHOR]

In this Paper the authors made work on the field of Health Sector that is Blood Analysis. Analysis of Blood is playing a very vital role as it is a parameter for detection of several kinds of diseases. Blood analysis speaks of a Frame that comprises of several ingredients which are essentially symbol for specific Blood disease. In the current work we are focused on finding out or predicting the Disease with a parameter of care. We made a utilization of various ML models for the prediction of disease. ML is a very popular approach nowadays for solving variety of problems and thus we made this approach useable for solving our problem. The data set on which we conducted experiment is a self-generated dataset. The Accuracy of the Model comes after performing Training on around 402 Samples. The Data Set used is a Physician Consulted Data Set. We in this paper will observe by means of graphs and figures several aspects of the Data Set and the ML approach. The Comparative Analysis of the approaches is discussed in the Conclusion Section. [ABSTRACT FROM AUTHOR]

The convergence of machine learning (ML) and the Internet of Things (IoT) has opened up new possibilities in healthcare, enabling the development of intelligent systems that can transform patient care. This paper presents an integrated healthcare system that harnesses the power of ML and IoT to deliver advanced medical technologies for accurate diagnosis and timely treatment. By integrating various sensors monitoring vital signs like heartbeat conditions, respiratory count, and temperature, real-time patient data is collected and transmitted to a server for storage. The collected data is then converted into readable signals using Arduino microcontrollers. In this paper, we propose the incorporation of predictive analytics, a machine learning technique, into the existing system. By analyzing patient data, predictive analytics algorithms can identify patterns, trends, and anomalies in vital signs, enabling early detection of potential health issues. We have used algorithms from Recurrent Neural Networks (RNN) like Support Vector Machine, Decision trees, and RandomForest. This empowers healthcare providers to intervene promptly and provide proactive treatment, ultimately improving patient outcomes [3]. The advent aim of the project would be using cloud servers, doctors can access the patient's condition using laptops or And roid phones, facilitating real-time communication. The integration of predictive analytics enables the system to provide decision support, assisting doctors in making accurate diagnoses and suggesting appropriate treatment plans based on the patient's symptoms, vital signs, and medical history. Furthermore, predictive analytics allows for risk stratification, prioritizing patients who require immediate attention based on their health profiles. By incorporating ML algorithms, specifically predictive analytics, into the IoT-based healthcare system, this paper seeks to improve the overall quality of healthcare services provided. The integration of advanced technologies and real-time communication between patients and doctors opens up new possibilities for enhancing patient care and treatment outcomes. [ABSTRACT FROM AUTHOR]

Current biomedical enhancers have a high widely recognized mode dismissal proportion. By the means of, debts are in lots of cases polluted with the aid of final electric cable impedance. Customary simple and computerized channels are recognized to smother ECG parts near the electrical cable recurrence. The quit of commotion in an electrocardiogram (ECG) is the focal point of this work, which changed into directed in MATLAB. ECG exam is a part of this assessment, and it involves 3 crucial levels at its middle. The initial step is to get the ECG signal that changed into recorded. Because the way the crude ECG was gotten contains various kinds of clamor, for example, impedance from electrical cables, pattern float, and cathode versatility concerning the patient, the subsequent stage includes eliminating these unsettling influences from the sign. This paper aims to introduce a technique for eliminating clamor from electrocardiogram (ECG) waveforms. The denoising of electrocardiogram signals is examined utilizing various methodologies. The paper presents and examines an original strategy for biometric ID in light of ECG information. The principal thought of the review is to apply Profound Brain Organizations (DNN) for human recognizable proof in light of the crude ECG signal. To further develop a general framework exactness different sign pre-handling and anomaly location strategies have been applied. [ABSTRACT FROM AUTHOR]

A cough is how the body responds once one thing irritates the throat or airways. Associate in nursing irritant stimulates nerves that send a message to your brain. The brain then tells muscles in the chest and abdomen to push air out of the lungs to force out the irritation. Cough may be a current clinical presentation in several metabolic process pathologies with a respiratory disorder, higher and lower tract infection (URTI and LRTI), atopy, rhino sinusitis, and post-infectious cough. Cough audio signal classification has successfully diagnosed a spread of metabolic process conditions. Cough classification plays a vital role in diagnosing and detecting disease at the first stage and checking out to stop or cure it and take needed steps at the first stage, which can save many lives. Thus, we've strived to attain analysis of cough audio signal to find abnormalities during this paper. In this paper, we've tried to separate cough audio signals taken from totally different people to search out the variation or abnormalities within the cough signal exploitation different feature extraction techniques, so the exploitation of different classifiersto search out the accuracy within the result. [ABSTRACT FROM AUTHOR]

The reliable operation of power systems is essential to prevent over-voltages, excessive currents, outages, and equipment damage, which can pose risks to users and lead to interruptions in the power supply. To address these challenges, a robust power safety system is required to swiftly identify, categorize, and pinpoint faults, ultimately reducing their impact. This paper presents a comprehensive approach for locating, classifying, and detecting faults within power systems. The primary objectives of this paper are to accurately identify and categorize different fault types occurring at various resistance levels and locations, gain insights into the causes of interruptions, expedite power restoration, and minimize recurrence. Additionally, this research aims to enhance understanding of the components of the protection system, enabling the implementation of preventive measures to reduce the likelihood of service interruptions and equipment damage. To achieve precise problem identification, classification, and fault localization, the proposed approach employs simple neural networks and efficient machine learning models. Leveraging the capabilities of neural networks, this technology yields precise results in power system fault analysis. [ABSTRACT FROM AUTHOR]

The continuous of purchasing online products, For the buy of any product, we must see the reviews of that product after that we purchase the products. Review places the main role in online purchasing the product. Mainly positive reviews make customers buy a product online, similarly, the negative reviews make confusion about whether the customer buys a product or not. The product seller wrote a fake review to satisfy the customer. Thus, identifying fake reviews is a faithful and ongoing research area. Identifying a fake review depends on key features of the review but also it depends on behave of the reviews. This paper proposes a Machine learning accession to the identification of fake reviews. In addition to the features eradication process of the reviews. This paper appeal to some features engineering techniques to essence various behave of the reviews. This paper equates the performance of some trail runs on a real dataset of online shopping reviews with and without features estimations of users' behavior. In both cases, we compare the performance of some classifiers: K-Nearest Neighbor (KNN), Navy Bayes, Logistic Regression, and Random Forest algorithm. [ABSTRACT FROM AUTHOR]

The main objective of image compression in the digital era is to reduce the size, of irrelevant and redundant data in digital images. this is able to keep and transfer the image in an efficient manner. In the present digital world, multimedia files have large sizes and consume a lot of space, and are also very difficult to transmit. Image compression plays a prominent role to reduce the size of the images and transmit such compressed data in a permitted time duration. This paper addresses image compression using machine learning methods. Machine learning is one of the subsets in artificial intelligence, in recent days machine learning plays prominent role in image processing. Using machine learning algorithms in image compression provides better-quality images and improves the different performance metrics. This paper attempts a study various machine learning algorithms as well as image compression techniques. [ABSTRACT FROM AUTHOR]

Various object detection models are presented in this paper. Definition of object is well defined in order to understand object detection models. Now-a-days the object detection which is computer vision and image processing related technology is widely used. Applications of object detections are infinite as it can be used all over like objects tracking, surveillance of videos, face recognition, detection of anomaly, counting of people, and the records still continue to many more. This paper compares various object detection algorithms the basic splitting is based on machine learning based and deep learning based. Fundamental objective of the paper focuseson Single stage object detectors (SSD) like You Only Look Once (YOLO) and Two stage object detectors as RCNN, Fast RCNN, etc. This paper's main goal is to highlight such object detection models and their use in various fields. [ABSTRACT FROM AUTHOR]

With most businesses increasing its dependency on the internet, it has become largely evident that several resources will be required to be either constructed or hired to sustain the growing needs of their businesses. Here comes the main requirement of usage of hired services like cloud computing which can be hired by many businesses for their continuous growth and sustenance. With the growing number of inventions and discoveries in the field of cyber-security, large number of attacks are also possible on the cyber assets. Cloud computing architecture is also no exception and is vulnerable with different breeds of attacks which threaten the utility of the cloud. Cloud computing architecture and the services it provides on cloud mainly use the feature of pay as you use with many of the services being auto scaled to a larger extent thereby required to maintain the flexibility for any growing business with a larger customer base. There are various fraudulent threats related to the energy utilization that attempt to exploit the elasticity of the cloud and the multi-tenant model it provides. In addition, with the rapid development in technologies, the networking systems are becoming complex which requires a detailed study on the various detection and mitigation techniques that are available against the different breeds of attacks on cloud paradigm. In this paper, an attempt is made to compile in detail various detection and mitigation techniques against a variant of DDoS called the fraudulent resource consumption (FRC) attacks. It also considers the review of different techniques applied in machine learning that train the models for detecting the attacks. This paper also provides the review of the studies based on use of SDN technique used for detecting these attacks using its inbuilt features. The paper concludes by proposing a set of recommendations for future studies concerning the identification and prevention of FRC attacks, leveraging SDN and machine learning. [ABSTRACT FROM AUTHOR]

This paper proposes a novel data-driven optimization framework that integrates building design decisions with simulation results to optimize sustainability across environmental, social, and economic dimensions. The building industry faces increasing pressure to reduce emissions and environmental impacts, but existing design tools do not fully account for how buildings will be operated by occupants. The proposed optimization framework utilizes statistical learning via metamodels and decision optimization to explore the complex tradeoffs involved in building decisions. The focus of this paper is on developing an optimization approach performed on common machine learning models such as classification and regression trees (CART), gradient boosted trees and random forest to investigate the causal relationship between building decisions and performance metrics. We integrate both descriptive and prescriptive analytics to inform an optimization model that provides decisions for both the design and operations of a given building. A case study is presented demonstrating the potential benefits of the optimization model in the design phase of a building. The integrative, data-driven optimization approach incorporates both categorical and continuous variables to create higher-performing, more sustainable building designs that better balance Planet, People, and Prosperity goals. [ABSTRACT FROM AUTHOR]

The digital twin concept is pivotal in Industrial 4.0, integrates physical and cyber spaces to address product design challenges. In the early design phase, digital twins offer valuable insights, while topology optimization, often integrated with digital twins, is prevalent during the optimization process. However, traditional topology optimization methods may fall short for soft robots due to their complex physical constraints and the high costs associated with physical simulations. Evolutionary algorithms, with their inherent adaptability, hold promise for autonomous optimization of soft robots based on available data. These algorithms are well-suited for generative design, potentially enabling data-driven generative approaches. This study introduces an intuitional evolutionary algorithm (iEA) to explore a generative optimization method for product design within the context of the digital twin. This paper presents a case study involving the design of soft robots for a speed competition in a simulated 3D environment, demonstrating the viability of combining the iEA with a data-driven generative design (dGD) model to develop a fast- walking soft robot. The results include learning curves and convergence diagrams, illustrating the efficacy of the design solution. The paper will also discuss information hierarchy, requirement analysis and functional modeling of the dGD model. [ABSTRACT FROM AUTHOR]

Palletizing is an important job in logistic operations. It is considered an extremely labor-intensive process when performed manually, as it includes awkward and repetitive working postures, which result in serious back injuries. This paper introduces a new assistive device that helps reduce the exposure to physical loads among palletizing workers. The risk factors associated with manual palletizing in the food industry were analyzed using the Rapid Upper Limb Assessment (RULA) method. Different postures were observed during manual palletizing in a food factory that had a RULA score of 7. An assistive device was proposed to improve the palletizing operation. As a result, after implementing the proposed assistive device, the RULA final score was dropped to 4 based on lifting at the elbow height of the pallet, 3 when lifting at low and medium heights of the loaded pallet during wrapping, and 4 when lifting at the highest point of the full pallet. In conclusion, the proposed assistive device helped improve the palletizing job and reduce its negative consequences for workers. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive analysis of Qatar Petroleum's maintenance system, focusing on its core objectives and the six integrated elements of Maintenance Best Practices spanning across upstream, midstream, and downstream production facilities. With an emphasis on the Centralized Maintenance Organization (CMO) concept, the study underscores its crucial role in achieving predefined outcomes. Our analysis centers on the primary goal of attaining best-in-class performance in Safety, Reliability, Integrity, and Cost. To achieve this, the maintenance system employs a cohesive set of processes and procedures, including workflow implementation, work selection via RBWS (Right Base Work Selection), meticulous planning and scheduling, and execution emphasizing quality and efficiency. Continuous improvement strategies, supported by Key Performance Indicators (KPIs), are instrumental in further enhancing system performance. Additionally, the maintenance system encompasses setting objectives, task prioritization, system stewardship, and overall performance enhancement. Through the integration of supporting elements such as equipment design, tools and procedures, information systems, and organizational structures, the system strives to achieve best-in-class safety, reliability, integrity, and cost performance (Bao et al.2023, Fatem et al.2022, Hamed et al. 2018) [ABSTRACT FROM AUTHOR]

The Industry 4.0 has significantly influenced the industry, compelling businesses to adopt to become more efficient, safe, reliable and environmentally responsible. Maintenance is recognized as a critical component in achieving these objectives, thus highlighting the importance of implementing effective maintenance policies. However, rapidly evolving nature of maintenance strategies introduces both challenges and opportunities in the selection and adaptation of fit for purpose maintenance policies. Increased availability and accessibility of Internet of Things (IoT) has accelerated the adoption of predictive maintenance technologies for shipping companies. Nonetheless, as the industry progresses towards prescriptive maintenance, launching such initiatives without a comprehensive strategy might present significant obstacles for shipping companies. This paper develops a strategic framework to investigate the potential of prescriptive maintenance paradigm in shipping industry. The study has strengthened with a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis. The study provides insights about challenges and opportunities to integrate prescriptive maintenance into shipping companies to increase its utility. [ABSTRACT FROM AUTHOR]

Recommender systems are among the utmost popular and successful uses of machine learning in the corporate world. Recently, these schemes have made use of ML methods from the artificial intelligence domain. Recommender systems are used to provide guidance to individuals in all fields of technology, and they are crucial for each domain. There are many different types of recommendation systems, such as content-based and hybrid-based, collaborative filtering-based. The collaborative filtering method is used in this paper for recommending the movies. In this field, the effectiveness of algorithms based on CF (collaborative filtering), which take advantage of user and item behaviour, has been established. In this paper, we have analysed five different similarity metrics on the dataset. The similarity metrics are cosine similarity, Euclidean, Pearson, Jaccard, and MSD with the KNN algorithm to predict outcomes. Results are evaluated by using different metrics which are RMSE, and MAE, and at the end there is a comparison between the item-based collaborative filtering and the user-based one approach to know which approach remains better. [ABSTRACT FROM AUTHOR]

Autonomous vehicle is a main direction of the current automobile development. The implementation of autonomous driving depends on the perception, decision-making, and control functions of the vehicle. Path planning is an important component of decision-making. Nowadays the path planning algorithm of autonomous driving system is greatly affected by the environment and cannot be applied to complex road environment. Based on this, this paper summarizes some path planning techniques, introduces the definition and importance of path planning, and expounds the commonly used path planning algorithms. This article provides an in-depth analysis of traditional methods, such as Dijkstra algorithm and A* algorithm, and discusses their advantages and limitations in handling real-world scenarios. The article also discusses some advanced technologies, such as Rapid-exploration Random Tree(RRT) algorithm and ant colony algorithm. These methods utilize advanced algorithms and machine learning to guide vehicles through complex and dynamic environments. In addition, this article also summarizes some improvement methods based on the above algorithms, such as combining Dijkstra algorithm with RRT algorithm to form Dijkstra-RRT algorithm. This paper can provide valuable insights into various path planning techniques for autonomous vehicles and assist in the further development of autonomous driving technology. [ABSTRACT FROM AUTHOR]

This academic paper explores the complex utilization of integrated task and motion planning (ITMP) in multi-robot systems. Given the rapid advancement of robotics and automation, multi-robot systems have gained increasing attention in various applications, such as smart manufacturing, logistics storage, environmental surveillance, and dual or multi-armed robotic platforms. The fusion of task and motion planning has become a key technology in the development of multi-robot systems. Initially, the paper suggests numerous strategies to integrate task and motion data to improve planning efficiency, thus boosting the motion planning efficiency of robots during task execution. It then investigates the use of learning techniques, such as reinforcement learning and deep learning, to enhance task and motion planning. Ultimately, it presents experimental evidence to support the claim that the integration of task and motion planning can effectively increase the efficiency of multi-robot systems and optimize their performance. The paper also examines modeling methods for multi-robot systems, typically incorporating task data with motion data to better guide robots during task execution. It presents shared space graph methods, sample-based planning methods, and market-based methods. Furthermore, it reviews learning models used to train and optimize machine learning models in ITMP. It introduces the INFORMED algorithm, a learning search issue in the field of task and motion planning that employs multilayer perceptron (MLP) and graph neural networks (GNN) to model relationships between streams and objects. Through this paper, the authors aim to offer valuable insights for the research and application of ITMP in multi-robot systems, thereby promoting further progress in this area. [ABSTRACT FROM AUTHOR]

In the modern age, a significant number of official documents, including IDs approved by the government and certificates, are predominantly image-based or paper-based. Extracting, entering, and searching for information from these documents manually can be a cumbersome and time-consuming process for organizations. To address this challenge, Automated ID Document Extraction and Classification (AIDEX) is introduced. This model utilizes machine learning algorithms to automatically classify identity documents issued by the government into predetermined categories such as AADHAAR card, Driving License and PAN card. By identifying unique indicators present in these ID proofs, AIDEX effectively detects and displays vital information, eliminating the need for manual intervention and achieving automation. Through appropriate access and permissions, the extracted data can be securely saved and accessed by authenticated users as required. The application of this strategy significantly reduces the time spent on physical labor, leading to resource conservation. Notably, AIDEX stands out by integrating the identification and data extraction from images of Driving Licenses, distinguishing itself as a novel approach. This paper provides a comprehensive overview of the objectives set for AIDEX, the various techniques employed during its development, and the technical specifications of the resulting model. The findings highlight the potential of AIDEX to streamline information retrieval processes, enhance operational efficiency, and promote automation in handling government-issued identity documents. [ABSTRACT FROM AUTHOR]