Smart Mobility Robot: Employing Line Follower Navigation for Object Movement

 

Introduction

The advancement of robotics technology has grownin recent years, offering substantial potential  across  various  industrial  sectors.  One  prominent  application  area  is  logistics, where robotic systems can play a vital role in automating material handling processes to improve operational efficiency and reduce human labor. Despite this progress, challenges remain in achieving improved accuracy, speed, and load-handling capabilities. Among the available  robotic  solutions,  the  line  follower  robot  stands  out  as  a simple  yet  effective approach for automating transportation tasks. Designed follower robots have been widely implemented in industrial settings due to their low cost, ease of deployment, and relatively simple control systems. 

The selection of a line follower robot in this study is driven by several considerations. First, the technology offers simplicity  and  efficiency,  making  it  well-suited  for  small-to  medium-scale  logistics operations.  Second,  it  is  cost-effective  and  composed  of  affordable  components,  which supports its use in resource-constrained environments. Third, it offers flexibility; route modifications  can  be  achieved  by  reprogramming  or  physically  altering  the  track.  Lastly, the  architecture  is  scalable,  allowing  for  future  upgrades  in  terms  of  payload  or  sensor integration. Several previous studies have explored the development of line follower robots. Ridarmin  et  al.  (2019)  proposed  a  prototype  utilizing  an  Arduino  Uno  and  TCRT5000 sensors for tracking a dark line, demonstrating basic autonomous navigation. Susilo (2018) introduced a prototype for automatic object delivery that incorporated a load cell sensor to determine the object’s weight and delivery destination, showcasing an early attempt at functional integration for logistics applications.

While  these  studies  laid the foundational  work,  challenges  remain  in  increasing navigation  accuracy,  improving  payload  handling,  and  optimizing  system  integration  for practical  use  cases.  This  study  aims  to  address  these  challenges  by  designing  and developing an autonomous line follower robot capable of transporting lightweight objects (up to 100 grams) along a fixed path. The proposed system integrates real-time navigation and load transport using an Arduino UNO microcontroller, BFD-1000 infrared sensors (as a more accurate alternative to TCRT5000), and an L298N motor driver for efficient motor control. 

The novelty of this work lies in its optimized design for power-efficient movement, enhanced  sensor  precision,  and  application  in  small-scale  logistics  environments to an area that remains under explored research. This approach is intended to contribute to the development of accessible and low-cost automation solutions for small and medium-sized enterprises (SMEs). The  development  of  smart  mobile  robots  based  on  line  follower  technology  has  been extensively studied and applied across various fields, particularly in logistics and healthcare industries.  This  technology  enables  robots  to  follow  predetermined  paths  using infrared sensors  that  detect  color  contrasts  between  the  line  and  the  background  surface. Mahendra  et  al.  (2019)  and  Hossain  et  al.  (2021)  demonstrated  that  line-following navigation systems offer high reliability in structured indoor environments and are relatively low-cost  to  implement.  In  the  context  of  object  transportation  automation,  this  approach has proven effective for tasks involving the delivery of goods or lightweight materials from one location to another without direct human involvement.

Beyond navigation  technology,  another  critical  aspect  of  such  robotic  systems  is  the ability  to  carry  or  push  objects.  Studies  by  Rathore  et  al.  (2019)  and  Kale  et  al.  (2020) discuss   the   design  of   actuators   and   robotic   mechanisms   to   lift   or   push   objects automatically. The integration of additional sensors, such as ultrasonic modules, has also been  explored  to  enhance  obstacle  detection  and  navigation  safety.  Recent  innovations even  incorporate  Internet  of  Things  (IoT)  connectivity,  as  discussed  in  Hossain  (2021), enabling  real-time  monitoring  and  control  of  the  robot.  Therefore,  a  line  follower-based robotic system equipped with object-handling capabilities presents a promising solution for efficient and adaptive internal transport automation.

Experimental Setup

In  this  study,  we design the  system  usingan  Arduino  UNO  microcontroller,  which functions  as  the  processor  for  both  incoming  and  outgoing  data.  The  components  are integrated into a single structural frame, including motorized wheels that serve as the base support  for  the  BFD-1000-linesensor,  which  is  responsible  for  detecting  the  navigation path.  The  frame  of  the  line  follower  robot  is  constructed  from  acrylic  material,  with  the robotic  arm  positioned  at  the  topmost  section  to  facilitate  efficient  object  pickup  and placement.  The  following  sections  present  the  system  block  diagram  and  the  workflow diagram of the object transfer robot based on line follower navigation.

The L298N driver is used to control both the rotational speed and direction of DC motors. It receives power from a 5V input, which can be supplied either through the 5V output of the microcontroller  or  from  a  step-down  voltage  regulator.  The  driver  receives  control signals from the microcontroller to determine whether the motor should move forward, turn, or  stop.  Additionally,  the  microcontroller  sends  speed  control  signals  based  on  the programmed instructions, allowing the motor to operate at the desired speed when moving forward or turning. 

The  BFD-1000  sensor  is  used  as  the  path  detection  component  for  the  line  follower robot.   A   total   of   five   BFD-1000-linesensors   are   employed   and   calibrated   using 
potentiometers. The calibration process is carried out to determine the appropriate infrared light intensity received by the photodiode sensor, enabling it to differentiate between high and low logic levels. This calibration is optimized for a sensor height of approximately 0.8 cm above the reflective surface.

Robotic Arm Design

The robotic arm is designed to assist in the picking and placing of objects. It utilizes four servo motors that function as the gripper and actuators for movement. The servo motors are  directly  connected  to  the  microcontroller  without  the  use  of  an  external  driver. The microcontroller sends control signals to the servo motors, instructing them on the direction and  angle  of  rotation,  thereby  enabling  the  robotic  arm  to  grasp  and  place  objects  as required.The  robotic  arm  is  assumed  to  consist  of nrevolute  joints  (rotary  joints),  each driven by a servo motor. The robot operates in a 2D or 3D environment. Each joint contributes an angular rotation denoted by θi, and each arm segment has a length Li.The base frame is fixed. For a planar 2D robotic arm, the end-effector position(x,y) is calculated using:

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A Generative Neural Network for Psychological Traits in Workplace Self-confidence: A Physics-inspired Approach

 

Introduction

The term “workplace self-confidence” describes an individual’s belief in their ability to carry out duties, make wise choices, and successfully handle obligations in a work environment. In addition to being a vital component of a person’s mental and emotional health, it also plays a significant role in determining their level of productivity, job performance, and general well-being. Psychological characteristics including self-awareness, self-efficacy (belief in one’s own abilities), emotional intelligence (capacity to regulate emotions and interpersonal interactions), and selfesteem are essential to professional confidence. These traits are dynamically influenced by both external environmental stimuli and internal cognitive states. High selfconfidence among employees increases their chances of taking on new tasks, taking part in decision-making, and proactively solving problems, all of which increase workplace productivity. 

Furthermore, by lowering anxiety, boosting resilience, and cultivating a positive view on career advancement, self-confidence promotes mental health. This is especially noticeable when workers feel overqualified and have creative self-confidence because they are more likely to act creatively and improve workplace performance. Research on the psychological effects of artificial intelligence (AI) reveals how incorporating AI into the workplace can affect workplace self-confidence by influencing personal traits like trust, anxiety, and selfefficacy. According to research, workers who have more self-efficacy and previous technological experience are more likely to trust AI, which increases their confidence in their ability to use AI tools efficiently. Like human trust, trust in AI is essential for enabling staff members to take an active role in task management and decision-making, which raises overall productivity. 

Dynamical systems within cognitive agent models are designed to simulate workplace environments, taking into account varying factors such as job demands, leadership styles, and social influences to predict and support psychological traits like self-efficacy and emotional regulation. These models have the potential to gradually boost workplace self-confidence by encouraging positive feedback through helpful AI-driven interactions. Additionally, ethical considerations in AI design are crucial for developing systems that improve user well-being by avoiding bias, regulating emotional states, and incorporating compassion, which promotes a more positive, self-assured, and productive work environment. Research on smart systems for cognitive computing demonstrates how AI can integrate fundamental cognitive functions like language processing and expert knowledge representation to resolve ambiguities in human-computer interactions. In order to improve cognitive processing and enable machines to assist complex decision-making with human-like reasoning and intuition, this collaborative intelligence model blends AI and human intelligence (HI). 

The study demonstrates the potential of neural networks in refining predictive models, highlighting their adaptability in diverse contexts. Building on this adaptability, the integration of ontology-based approaches, as discussed in, offers a novel pathway to enhancing psychotherapy interventions, showcasing the versatility of computational intelligence in varied domains. Similarly, the work in highlights how ensemble learning can effectively handle high-dimensional data, enabling precise classification and prognosis in complex scenarios. In a related vein, the use of handwriting analysis in personality assessment, as illustrated in [15], underscores the potential of machine learning in psychological profiling,
emphasizing its broad applicability across disciplines. Furthermore, the study in [16] exemplifies how neural networks can be leveraged for accurate forecasting in energy generation systems, demonstrating their efficacy in addressing practical challenges beyond traditional boundaries.

Neural network architecture

The neural network model, as depicted in Fig. 1, is structured to simulate the progression and stabilization of psychological traits by integrating dynamic cognitive states. This architecture consists of an input layer, a hidden layer utilizing physics-inspired transformations, and an output layer to generate adaptive temporal traits. The input layer processes three core cognitive statesself-esteem, self-efficacy, and self-concept-capturing their fluctuations due to environmental factors and internal feedback. These cognitive states act as the foundation for generating complex psychological traits. Moving through the hidden layer, the model incorporates a Maxwell-Boltzmann distribution to represent how these cognitive states fluctuate
initially, akin to the dispersion of particle speeds in physics. This distribution allows the model to simulate initial instability in cognitive states before they begin to settle. To further shape the output, a sigmoid function is applied within the hidden layer, introducing non-linear scaling that drives the cognitive states towards equilibrium. 

Motivation, Learned Helplessness, and Social Anxiety are the final long-term psychological traits produced by the output layer, which represent the consistent results of the underlying cognitive processes. The traits that are produced reflect the way that cognitive states stable and change over time, providing information about how both adaptive and maladaptive traits evolve in response to work environments. A visual flow from initial inputs reflecting cognitive states through their distribution and change inside the hidden layer to the appearance of psychological traits in the output layer may be seen in Fig. 1. The physics of stability and stabilization can be used to form complex psychological traits in a structured neural network, as this model provides. In this model, interpretability is achieved by leveraging the Maxwell-Boltzmann distribution, which provides a statistically interpretable representation of variability within cognitive states, such as self-esteem, self-efficacy, and selfconcept. 

This distribution operates within an equilibrium framework that visually illustrates how each cognitive state stabilizes over time. By framing cognitive fluctuations as distributions converging toward equilibrium, the model makes the dynamic stabilization process both transparent and accessible, enhancing interpretability.

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Hybrid Deep Learning for Climate Prediction with Temporal, Spatial, and Environmental Data

 

Introduction

Climate change is one of the most urgent and complex challenges faced by humanity today. Its widespread impact is felt across ecosystems, economies, and human societies, altering the natural balance that sustains life on Earth. Rising global temperatures are melting glaciers, increasing sea levels, and intensifying extreme weather events. Also the biodiversity loss and agricultural disruptions threaten the stability of ecosystems and food security. These changes demand a deeper understanding of the Earth’s climate dynamics to predict and mitigate their consequences effectively. Traditional climate models, based on statistical and numerical approaches, have laid the foundation for understanding climate patterns. However, they face significant challenges when applied to the large, diverse, and interconnected datasets generated by modern climate monitoring systems. These limitations highlight the need for advanced computational models that can comprehensively analyze climate data and provide actionable predictions.

Understanding climate data complexity

Climate data is inherently multidimensional, capturing the interactions between temporal patterns, spatial variations, and human-induced influences. Each dimension offers unique insights into the processes shaping the Earth’s climate system. Time series data, such as temperature, precipitation, and greenhouse gas concentration measurements, are essential for understanding long-term trends, detecting anomalies, and forecasting future states. These datasets reveal patterns of global warming, seasonal fluctuations, and extreme weather events. However, temporal data alone cannot provide a complete picture, as it lacks information about how these changes vary across different regions or ecosystems.

Spatial data, such as satellite imagery, complements temporal datasets by offering a detailed view of the Earth’s surface. High-resolution images capture phenomena such as deforestation, glacier retreat, urban expansion, and vegetation health. These datasets allow researchers to assess the direct impact of climate change on specific regions and ecosystems. Their sheer volume and complexity present challenges in extracting meaningful insights. Advanced machine learning techniques are required to process these high-dimensional datasets and detect subtle changes that are often overlooked by traditional approaches.

Socioeconomic and environmental indicators provide another critical layer of information by linking human activities to climate change. Indicators such as CO2 emissions, energy consumption, urban development, and deforestation rates highlight the anthropogenic drivers of climate dynamics. These indicators also reveal the socioeconomic consequences of climate change, such as resource scarcity, economic instability, and public health challenges. Despite their importance, integrating these indicators with temporal and spatial data into a unified framework remains a complex task. This work requiring innovative modeling approaches that account for interactions between diverse data types.

Significance of the research

This study holds significant potential for advancing climate prediction by integrating multiple data dimensions into an unified framework. Existing models are often limited in their ability to holistically analyze interconnected factors or provide interpretable outputs. By leveraging advanced deep learning methodologies, such as TCNs for time series data, CNNs for spatial data, and Explainable AI for interpretability, this research seeks to address these limitations comprehensively.

One key outcome of this research is improved prediction accuracy, which enables more precise forecasts of climate variables like temperature and precipitation. These predictions are vital for developing effective strategies to mitigate and adapt to climate change impacts. Enhanced transparency, achieved through Explainable AI techniques, ensures that model outputs are interpretable, fostering trust among policymakers, researchers, and the general public. Additionally, actionable insights derived from the integrated framework empower stakeholders to implement targeted interventions. Such as optimizing land use policies, mitigating deforestation, or improving urban planning to address climate risks.

This unified approach bridges gaps between diverse data modalities, enabling a deeper understanding of climate dynamics. By providing a scalable and interpretable solution, this research contributes to global efforts to combat climate change, supporting data-driven strategies for sustainable development and environmental preservation.

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An Intelligent DMI-based Feature Selection Approach for Measuring Customer Loyalty using SVM

 

The business competition among different companies has exponentially increased in recent years. To remain in business, there is a pressing priority for an increased focus on customer satisfaction that ultimately fosters customer loyalty. The customer loyalty analysis is critically important to retaining current customers and attracting more new customers. The proposed study focuses on an efficient approach to determining the customer’s loyalty and satisfaction with a product. This is determined by using machine intelligence and sentiment analysis of a large dataset of product reviews which is obtained through Amazon. The novel Feature Selection Method is performed to improve performance for large data sets. This feature selection method is based on Dynamic Mutual Information (DMI) which helps in selecting only important features to reduce the dimensionality problems of very large datasets. Text preprocessing is performed initially, which includes Stopword removal, token, and lemma creation. SentiWordNet along with the Intelligent SVM technique is implemented for aspect-level sentiment analysis to categorize customer reviews into three different classes of loyalty. 

Introduction
Recent advances in internet facilities have revolutionized and digitally transformed modern society. Recent innovations in technology have improved people’s lives by providing online banking, education, buying, and selling facilities for different products and services [1], online sales have increased instead of going to the mart or store for shopping. The pandemic has changed the way of shopping surprisingly. Nowadays, the e-commerce industry is growing rapidly by providing many online shopping websites. These sites include Amazon, eBay, Ali Express, and many more. One of the leading e-commerce websites, Amazon has had more than 4.7 billion trades in the past year with more than 400 million active customers [2] such e-commerce shopping sites create more comfort for users and sellers. However, some difficulties are there in these processes. One of the main issues faced by users is selecting trustworthy sellers for the best products. There is a need to provide a platform through which users get the best products according to their choices. This can be achieved by providing customer reviews to users on social networking sites [3, 4]. These reviews and feedback can assist new customers with better product selection.

Literature Review
Among the available techniques in literature, the first technique works with subjective reviews and the other works with objective reviews. The proposed study uses subjective reviews to extract sentiment scores from the SentiWordNet dictionary. The polarity of aspect level reviews is calculated which are Positive, Negative, and Neutral. The proposed technique works with an intelligent SVM algorithm to extract the overall customer loyalty level toward a product. 98.7% accuracy is achieved for aspect-level sentiment analysis [18]. Document-level Sentiment sorting is performed on a movie reviews dataset to analyze the sentiment levels of users for different movies [26]. Table 1 shows an overview of similar works identified in the literature.

Research Methodology
In the proposed research, the sentiment score of user reviews for products is evaluated in different steps. In the first step, customer review data of products is obtained from Amazon. DMI calculates the entropy of two variables, measures the relevancy of variables with each other, and assigns a score. SentiWordNet library is used to calculate the polarity scores of the selected features to obtain the sentiment level of the aspect. An aggregate score of the polarities is calculated to identify the overall customer loyalty toward the product. A support vector machine algorithm is used to classify the reviews based on constructing a Hyperplane to segregate classes. The better the Hyperplane the better the classification process will be. SVM's advantage is that it can treat outliers efficiently.

Polarity Analysis of Reviews
In the fourth step, POS tagging is applied to generate tags for individual terms. In the fifth step, DMI is used to generate a more reduced set of features so that performance is improved at the end. Entropy is calculated, and only those terms are considered that have values higher than the threshold value of 0.05. In the sixth step, SentiWordNet calculates the polarity score of only those terms that are filtered out, in this way only important terms are used in sentiment analysis. In the seventh step, the Sentence token score per word is computed. The sentence token score of important tokens is calculated to calculate the aspect level sentiment score as shown in equation (4).

In Step eight, an accumulative score of the sentence words is calculated using equation 1, in which the aggregate score is calculated. In step eight, the review level customer loyalty score is calculated to classify into positive, negative, or neutral classes. The review shown in Table 5 is obtained through the SentiWordNet dictionary. The last three rows in Table 5 show the percentage of review characteristics in different classes and the review is classified as positive review which is the highest percentage among all three classes.

Experiments and Results
The proposed methodology produces results in Rapidminer. SentiWordNet and SVM algorithms are used to generate results and predict customer loyalty. Input reviews are obtained from Amazon which are parsed, tokenized, and lemmatized. 5000 reviews are obtained about two different Samsung mobile phones. After preprocessing, features are extracted using a mutual information scheme, in which important features are filtered out using entropy. SentiWordNet 3.0 is used for PoS tagging and polarity score calculation. In this section, the sentiment analysis process and customer loyalty prediction using a single review are explained in Tables 4 and 5 for easy understanding. The aspect level sentiment score is calculated, and this score is aggregated to calculate the overall sentiment level of the customers. The percentage of positive negative and neutral reviews of 1000 reviews is calculated by using the following formula and presented in Figure 4.

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Artificial Intelligence inEducation: Enhancing Learning Experiences and Personalization

 

The use of Artificial intelligence (AI) for educational purposes is examined in this assignment, along with how it might improve and personalize learning. The use of AI allows for the personalization of learning paths for each student, the identification of performance gaps, and the delivery of focused interventions. The literature review focuses on how AI can be used to optimize learners' progress toward autonomy, promote metacognitive acquiring knowledge and self-regulated practices and support personalized language acquisition. With the use of AI technologies, which offer personalized learning materials, online interaction, and flexible learning pathways, students may take charge of their education. But there are also discussions about issues like the necessity for human interaction, data privacy, and ethical issues. The findings imply that in order to ensure the successful application of AI, ethical considerations must be carefully considered and continually assessed. The study finds that artificial intelligence (AI) has the ability to revolutionize education, but it also recommends more research to fill in any gaps and enhance applications in the future.

Introduction 

The usage of complex formulas and machine learning methods developed by artificial intelligence (AI) to automate activities, improve decision-making, and expand overall efficiency has revolutionized several industries. In the sphere of education, AI has the force to revolutionize conventional teaching approaches and empower both teachers and pupils. AI can form personalized wisdom ventures that are catered to each student's necessities, skills, and welfare by analyzing vast amounts of data and retrieving insightful acquaintances. The deconstruction aspires to research the integration of AI technologies into academic grounds to enrich the education procedure and handle the myriad knowledge necessities of learners. By leveraging AI, tutors can achieve practical perspicuity in pupils' advancement, pinpoint proficiency voids, and supply targeted interventions to stimulate adequate learning results. The scholarly journals review will enlighten the diverse research areas on this topic pinpointing the major trends, issues, and further prospects. Applying the appropriate method the results will be presenting the key developments in this study.

Review of literature

According to Chen et al. 2021, the rapidly growing trend of utilizing AI in the educational field has created a new space for innovative research studies. This machine-based algorithm is highly capable of making suggestions, and forecasts and even has decision-making agility. In the arena of schooling and education, AI can generate quality theoretical innovations with myriad applications and pedagogical marks. The paper underlines the function of AI tools in enabling personalized terminology learning. It underscores the prospect of AI to acclimate command and supply tailored and quite accurate data and feedback to students, thereby managing their demands and nurturing better education techniques and developments(Chen et al. 2021). The technology even can track a student’s overall growth, and understanding and can deliver recommendations accordingly with its high-end feature of natural language processing and intelligent tutoring systems.According to Chen et al. 2022, theintegration of modern AI trends leverages the practice of personalized language acquisition. It has a huge potential of adapting personalized instruction and recommendation-generating patterns that can dynamically handle the entire procedure of monitoring and tracking the understanding level of students and deliver scaffolding to each learner. The paper also has highlighted the role of technology in fostering metacognitive learning and self-regulated practices.

Even arising issues are identified by the authors such as data privacy interruption, unethical practices of Artificial Intelligence, the lack of practical training etc. The prospect of AI in optimizing the learners toward autonomy is another topic covered in the paper. Learning resources can be accessed and self-directed learning is made possible through these technologies(Chen et al. 2022). The ability to explore content based on competence level and educational choices is made possible by customized suggestions  and adaptive learning routes. This autonomy encourages learners to take control of their education, fostering accountability and self-control.According to Mohammad Ali 2023, ChatGPT stimulates pupils to contend in liberated language exercises and quests. The AI tool gives students to access outside of the learning environment, enabling possibilities for ongoing schooling and individualized language evolution. With the autonomy equipped by AI tools like ChatGPT, learners may take charge of their academic ventures and hone self-regulation mastery. The article does, however, also admit significant tribulations with employing AI in language learning(Mohammad Ali 2023). When using AI technologies in education, it's vital to take into account issues like how poorly AI comprehends subtle linguistic and cultural distinctions as well as issues with data privacy and morals.

Recommendations

For conducting more effective research on this topic of AI integration in an educational context, several recommendations can be made. The study should incorporate primary data analysis besides reviewing secondary journals and research papers. It should conduct surveys and interviews engaging in active interaction with educators, administrators, and even the learners to receive insights on the practical usefulness or limits of AI integration in learning practices. Further, the long-term impacts of AI on pupil achievement outcomes can be gained by conducting longitudinal studies. Throughout the research process, strict ethical guidelines must be observed to protect the well-being, privacy, and rights of all participants..

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A Logic Petri Net Model for Dynamic Multi-Agent Game Decision-Making

 

This study proposes a logical Petri net model to leverage the modeling advantages of Petri nets in handling batch processing and uncertainty in value passing and to integrate relevant game elements from multi-agent game processes for modeling multi-agent decision problems and resolving optimization issues in dynamic multi-agent game decision-making. Firstly, the attributes of each token are defined as rational agents, and utility function values and state probability transition functions are assigned to them. Secondly, decision transitions are introduced, and the triggering of the optimal decision transition is determined based on a comparison of token utility function values, along with an associated algorithm. Finally, a dynamic game emergency business decision-making process for sudden events is modeled and analyzed using the logic game decision Petri net. 

Based on reachable markings, reachable graphs are constructed to analyze the dynamic game process. Algorithms are described for the generation of reachable graphs, and the paper explores how the logic game decision model for sudden events can address dynamic game decision problems, generate optimal emergency plans, and analyze resource conflicts during emergency processes. The effectiveness and superiority of the model in analyzing the emergency business decision-making process for sudden events are validated. A sudden event is an emergency that poses direct risks and impacts human health, life, and property, requiring urgent intervention to prevent further deterioration. These intervention measures are organized into a process, which is typically described in an emergency plan and referred to as the emergency response process. 

In this process, all emergency personnel are dedicated to managing disasters to minimize or avoid the secondary impacts of the disaster. Generating better contingency plans before emergency responses have become an urgent issue to address. The uncertainty of evacuation time during emergencies, and its stochastic analysis was conducted by coupling the uncertainty of fire detection, alarm, and pre-movement with evacuation time.The forecasting model is event-dependent and takes into account many social and environmental elements regarding different sorts of events, such as socio-economic situations and geographical features. This is due to the great range of emergency occurrences, including both natural and man-made ones. The business decision-making process in disaster operations management varies greatly depending on the type of occurrence, taking into account factors like severity, impacted region, population density, and local environment, among others. 

There are many different types of hazards present worldwide. The health of vulnerable people is placed at risk by natural, biological, technological, and sociological dangers, which also have the potential to seriously impair public health. For instance, the authorities in-charge of providing clean water are responsible for the prevention of waterborne illnesses, while law enforcement and road transportation agencies are in charge of reducing traffic accidents. Zoonotic illnesses (diseases spread from animals to people) need coordinated action from the agricultural, environmental, and health sectors. These increases in new or reemerging diseases are attributed to a number of factors, including global warming, low vaccination rates in high-risk and vulnerable populations, growing vaccine resistance and skepticism, rising anti microbial resistance, and expanding coverage, frequency, and speed of international air travel. A professional who develops plans for emergencies, accidents, and other calamities is known as an emergency management director. Directors of emergency management work together with the leadership team of an organization to evaluate possible hazards and create best practices for handling them. Designing emergency procedures and developing preventative actions to lessen the risk of emergency circumstances occurring fall under their purview. Directors of emergency management play a crucial part in ensuring the safety of all employees and equipping staff to act effectively in case of an emergency. Plans for disaster preparation choose appropriate organizational resources, lay down the tasks and roles, establish rules and processes, and plan exercises to increase preparedness for disasters. The effectiveness of the response activities is improved when the needs of populations affected by catastrophes are anticipated. The effectiveness of the response operations is increased by increasing the ability of workers, volunteers, and disaster management teams to deal with crises. Plans could consist of the following: Sites for temporary refuge, and routes for evacuation water and energy sources for emergencies. Additionally, they might talk about stockpile requirements, communication protocols, training plans, chain of command, and training programs. One of the most crucial metrics for gauging the effectiveness of an evacuation is the time it takes. 

Residents who are detained for an extended period of time represent a serious threat to staff safety because of the unpredictability of events. A building’s inhabitants who attempt to flee during a fire accident exhibit a range of response times (RTs) between the time they are given a warning and the decision to leave. A number of complex factors, such as occupants’ familiarity with evacuation routes, their ability to operate evacuation amenities and fire protection apparatuses, the number of people in the area,and occupants’ psychological and physical conditions and behaviors, can affect how affected personnel are evacuated from a disaster site. Different factors have an impact on evacuation time (ET). The results indicate that it is a variable influenced by a significant number of uncertain factors, including emergency evolution dynamics, human behavior under emergency conditions, and the environment. The benefits of developing appropriate emergency response plans using safety and industrial hygiene resources to mitigate or prevent harm to factory personnel and nearby community residents caused by chlorine gas leaks. Everyone on the team has to be knowledgeable on how to spot leaks and react to them in order to keep the employees safe when handling chlorine. Since chlorine has a strong, unpleasant scent that resembles that of a potent cleaning solution like bleach, most chlorine leaks are quite easy to detect. Every facility that works with chlorine has to have an emergency kit on hand. This kit should include a variety of tools that may be used to stop or limit leaks around plugs, valves, or the side wall of a tank or cylinder used to store chlorine. Breathe in some fresh air and leave the location where the chlorine gas was emitted. If the community has an emergency notification system, be sure they are familiar with it. For directions, consult local authorities and emergency bulletins. If the chlorine discharge occurred outside, seek protection inside. 

To ensure that the contamination does not enter, make sure all windows are closed and ventilation systems are off. Leave the location where the chlorine was discharged if you are unable to get inside. Get outside and look for higher ground if the chlorine discharge occurred indoors. Open the windows and doors to the outdoors if the chlorine leak was caused by chemicals or home cleaners to allow infresh air. We focus on agent-based problem-solving strategies with business decision-making capabilities for CSC, which are based on Multi-criteria business decision-making methods (MCDM) methods for dealing with automated selection in CSC and PN techniques for modeling such context. Petri nets are used as modeling tools in the discrete-event dynamic process known as the multi-agent system. In comparison to alternating current micro grids, direct current micro-grids stand out for their ease of control and power management. They also offer a number of benefits, including higher conversion and transmission efficiency, greater reliability even in re-mote locations, convenient control, lower costs, and less filter effort due to the absence of reactive power, phase synchronization, high inrush current, etc. A rational actor must interact if enhancing subjective utility necessitates interaction with other agents. If there is contact between rational agents, at least one of the agents is trying to maximize his utility. Agents collaborate if their aims are the same. If their aims conflict, they engage in competition. 

The majority of these interactions occur between these two extremes. An interacting agent would do well to predict the objectives of other agents. A more well-informed actor may foresee some aspects of how other agents will act in response to their objectives. In these situations, strategic thinking is required. A contact in which strategic thinking occurs is referred to as a strategic interaction (SI). In game theory, SI or games are examined. The game theory takes into account reason and the potential to forecast rational behavior. The existence of widespread awareness of reason is assumed. This implies that each participant in an interaction believes in there a son of the others and that they, in turn, believe in his rationality, and so on.The equilibrium is the expected behavior of players or participants in an interaction. If one of the players strays from equilibrium, nobody wins. Because of this, it is termed equilibrium. In finite games, there is at least one equilibrium. At least two application agent and mechanism designs are required for artificial intelligence games. We have a game in agent design and must calculate appropriate behavior. We have an expectation about the behavior and must develop game rules in mechanism design. These two goals can be addressed theoretically by running algorithms over a game tree, or practically by creating an environment in which various real players can interact. Most games are written in low-level programming. Game rules are more easily editable. Algorithms may be created that change game representation in every way imaginable, such as ‘reduce number of players’ or ‘remove simultaneous turns’. 

Game representations may also be used to create evolutionary mechanisms. Logical Petri nets can further simplify the network structure of real-time system models, making it easier for us to analyze the properties of the system at a conceptual level, while also alleviating the problem of state space explosion to some extent. Petri nets can not only characterize the structure of a system but also describe its dynamic behavior. Currently, many scholars have proposed extended forms of Petri nets, such as logical Petri nets, timed Petri nets, and colored Petri nets, and their applications are becoming increasingly widespread. Multi-agent games involve multiple elements, such as players, strategies, utilities, and information equilibrium. The existing modeling elements of logical Petri nets cannot accurately describe these elements, so improvements need to be made to logical Petri nets. Based on the existing modeling elements of logical Petri nets, modifications or additions of new modeling elements are needed to model game elements, enabling the new model to accurately describe dynamic game problems in multi-agent systems. 

We consider a mean-field game (MFG)-like scenario where a large number of agents must select between a set of various potential target destinations. This scenario is inspired by effective biological collective decision mechanisms such as the collective navigation of fish schools and honey bees searching for a new colony. The mean trajectory of all agents represents how each person impacts and is impacted by the group’s choice. The model can be seen as a stylized representation of opinion crystallization in a political campaign, for instance. The initial spatial position of the agents determines their biases initially, and then in a later generalization of the model, a combination of starting position and a priori individual preference. The existence criteria for the specified fixed point-based finite population equilibrium conditions are developed. In general, there may be several equilibria, and for the agents to compute them properly, they need to be aware of all the beginning circumstances.

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Spatial federated learning and blockchain-based 5G communication model for hiding confidential information

 

At present, the preferred method of transmitting a rapid blockchain message is to send several transactions, constituting a covert 5G communication technique. However, this approach is inadequate for processing larger quantities of sensitive data, and the potential for losing confidential information is significant. Additionally, the sender’s identity is not concealed. Despite the high embedding rate of steganography techniques, they are increasingly vulnerable to detection and statistical feature-based analysis. This investigation suggests a covert blockchain communication methodology that incorporates spatial federated learning and spatial blockchain as a means of fixing these issues. By utilizing Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to encrypt the sensitive document and uploading it to the Inter Planetary File System (IPFS), the technique conceals sensitive files and the sender’s identity. Then, using image steganography based on Generative Adversarial Networks (GAN), the sender implants the hash value of the encrypted document into a carrier image. After uploading the encrypted image to IPFS, the sender creates a transaction with the hash value of the encrypted image. This transaction is then signed by a ring signature and broadcasted to the blockchain network for verification and confirmation. The recipient retrieves the encrypted document and decrypts it according to the access control policy established by CP-ABE. According to experimental findings, this model can increase the volume of sensitive data transmitted from KB to MB while providing higher confidentiality and security.

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Exploring the Profound Influence of Machine Learning on Business Intelligence: A Comprehensive Review

In the dynamic landscape of data-driven decision-making, the intersection of Machine Learning (ML) and Business Intelligence (BI) has become a pivotal arena, propelling organizations toward more informed and strategic insights. The fusion of these two domains is characterized by a continuous evolution, marked by innovative trends that redefine how businesses extract value from their data. This synergy between ML and BI not only augments analytical capabilities but also transforms raw data into actionable intelligence, empowering organizations to navigate the complexities of the modern business environment. 

As we delve into the emerging trends in ML and BI integration, it is evident that the convergence of advanced analytics and business intelligence is ushering in a new era of efficiency, automation, and foresight. From augmented analytics and predictive modeling to the democratization of machine learning through automation tools, the landscape is evolving rapidly. This exploration will delve into key trends shaping this amalgamation, offering a glimpse into the future of data-driven decision-making where insights are not just discovered but dynamically generated, enabling businesses to stay ahead of the curve and make strategic decisions with unparalleled precision. 

The preponderance of technology is focused on the creation of value in businesses. Technology is a tool that creates value, and companies exist to facilitate the exchange of value between people. Technology is a tool that allows businesses to trade values more effectively and efficiently and create new values that may be shared, as explained above. Technology has a significant impact since it is continuously developing. Several areas for different sectors enhance how they work, especially machine learning, which helps businesses enhance their business process. Machine learning helps businesses make decisions as it has a strong relationship with business decision-making. 

The contribution of machine learning in companies is essential since it has a strong relationship with business intelligence and helps organizations make better decisions when it comes to decision-making. Without machine learning, business intelligence is ineffective indecision-making, and company leaders cannot make successful decisions without machine learning.Business intelligence (BI) is referred to as converting data into information, subsequently transformed into knowledge. When it comes to business intelligence (BI), the goal is to make better, more informed choices. Business intelligence assists businesses in collecting and analyzing data to detect trends and patterns. This information may then be utilized to enhance strategic planning, operational efficiency, and marketing initiatives, among other things. One of the most significant advantages of business intelligence is that it may assist firms in reducing waste and optimizing resources. An organization that determines that it is selling things that are not in great demand, for example, might change its inventory levels to reflect this information. Alternatively, suppose a company notices that a particular product is being returned at a higher rate than others. 

In that case, it may look into what could be causing the issue and take appropriate action. Organizations may also benefit from business intelligence in terms of improving customer service. Businesses may better know what consumers are searching for by watching their activity over time and analyzing the data. If a firm notices that its consumers are unhappy with its service, it may rectify the situation and improve customer satisfaction. Business intelligence (BI) is now critical component of many firms' day-to-day operations. Businesses benefit from it because it improves decision-making and helps them better understand their goods and services. The better fulfilling consumer wants, increasing sales, providing better service to customers, lowering expenses, maximizing resources, and minimizing waste improve firms' bottom lines. Recently, we've observed integrating machine learning capabilities into business intelligence systems, making BI considerably more successful at uncovering hidden insights. BI solutions that can efficiently combine these skills in a user-friendly manner will soon become the standard. As consumers get used to this feature, they will expect it to be available at all times. 

GPS and other technology that we now can't fathom our lives without are examples of this. Combining these capabilities automates the process of unearthing insights that business users were not aware were available until they were discovered. For example, on a typical dashboard, a business user looking at their top-line sales would conclude that the trend seems to be in good shape and that there is no need to investigate deeper. However, there may be grounds for worry in the fine print, in the underlying makeup of the sales figures, which is difficult to discern. Some items may be doing well, while others may be exhibiting a deterioration in performance. This critical understanding is concealed from public view. Additionally, automation of this process results in insights being supplied much more rapidly, enabling the company to respond quickly and with better information. It allows the business to act faster and with better information. 

Automating these procedures should allow the analyst to spend more time on other responsibilities in their organizations. Many analysts are engaged in regular chores such as variance analysis, the search for anomalies, and the creation of comments for inclusion in reports, among other things. The analyst will devote more time to higher-value activities.In data analysis, machine learning models successfully uncover hidden patterns and insights. For many decades, data professionals have used these strategies to tackle technical and challenging business challenges. Because of improvements in computing power, it is now possible to construct and execute these complicated mathematical models on a more accessible platform. Models that used to need costly, high-end technology can now run on commodity platforms accessible to everyone, regardless of their financial situation. 

Machine models are categorized into Supervised, Unsupervised, Semi-supervised Learning, and Reinforcement Learning models, and these models have several algorithms which can be used for Business intelligence (BI) such as Feedforward Neural Network (FNN), Artificial Neural Networks (ANN), Support Vector Machine (SVM) algorithm, KNN algorithm, etc. This paper will perform a comprehensive review of machine learning models used in business intelligence. Furthermore, we will review the impact of machine learning on business intelligence.

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An Efficient Machine Learning Prediction Method for Vehicle Detection: Data Analytics Framework

 

The availability of transportation is considered a significant hallmark of a developed society. Since the evolution of the human species, the imperative to relocate from one location to another has been a fundamental requirement. At present, there exists a plethora of transportation options in Indonesia. However, most individuals favor road transportation due to its ease and convenience. The rise in population has led to a corresponding increase in the number of vehicles on the roadways. Hence, it presents a challenge for security authorities and governmental bodies to oversee all automobiles' mobility across various locations effectively. 

The present study proposes a methodology for detecting and tracking vehicles using video-based techniques. The process's initial stages involve preprocessing, including frame conversion and background subtraction. Next, the process of detecting vehicles involves the utilization of change detection and a model of body shape. Subsequently, the next stage entails the feature extraction process, focusing on extracting energy features and directional cosine. Subsequently, a technique for optimizing data is employed on the vector comprising excessively extracted features. The methodology integrates a data mining technique based on association rules, which is subsequently complemented by a random forest classification algorithm. The approach generally integrates multiple methodologies to attain effective and precise identification of automobiles in video-derived datasets.

Traffic disruption is a prevalent issue in Indonesia, particularly in the province of Special Capital District (DKI) Jakarta. The authorities have implemented multiple measures to mitigate traffic disruption in Jakarta. One of these initiatives involves the establishment of the Jakarta Smart City information system. The Jakarta Smart City information system harnesses closed-circuit television (CCTV) data from multiple sources, such as the Transportation Agency (DisHub), Bali Tower, the Public Works Service (PU), and Transjakarta, among others. Around 6,000 CCTVs are distributed across the Jakarta region, with their real-time data being transmitted and displayed on the portal of the Jakarta Smart City system. Quick detection of vehicles becomes necessary to provide inattentive drivers with sufficient time to avoid traveling conflicts and thus minimize the likelihood of rear-end collisions. Moreover, the current techniques for traffic surveillance that count automobiles using electric circuits on the road are costly. All of these factors necessitate the investigation of novel and favored techniques for the vehicle recognition task. Typically, the primary objective of detecting vehicles is to identify potential vehicle positions within an image and designate them as areas of interest (A.O.I.) for subsequent processing tasks. In contrast, computerized automobile identification is a complicated and intrinsically tricky task.

To detect moving vehicles on avenues, reliable systems and programs with efficient extraction methods are required. Real-time traffic inputs produce an enormous volume of data every day; to manage such a large quantity of data, artificial intelligence (A.I.) and computer vision methods are combined to improve the precision of the framework. This recent technological advancement has reduced human and labor needs. A robust video-based surveillance apparatus must be adaptable to the environment's behaviors. However, threats such as trembling cameras and noise interference still exist. Recognizing vehicles during the day is difficult because lengthy reflections cast by the sun can lead to misclassification or interference. In contrast, night vision detection presents difficulties due to the lack of adequate enlightenment, making it difficult for the classifier to identify effectively. Identifying target motion using artificial intelligence (A.I.) technology is one of the foundations of automobile environment sensing. Moving objects in conveyance typically refer to automobiles or individuals available in operating conditions. Additional immobile things, including transportation systems and vegetation, are typically called landscapes. 

To obtain the desired format, it is necessary to distinguish moving components from the background contemporaneously by examining the video input footage extensively. Diverse strategies were employed to establish technologies capable of detecting, counting, and classifying automobiles for use in automated transport platforms' traffic tracking. This section addresses the subject matter of these kinds of systems and an understanding of the methodologies used in creating them. Naz et al. presented a video-based actual time tracking of vehicles using the optimized simulated loop methodology. The researchers utilized real-time traffic monitoring equipment installed along roads to determine the number of vehicles that traveled on the road. In this approach, accounting is done in three stages by monitoring the vehicle's movements throughout an imaginary loop monitoring zone. Ukani et al. presented an alternative video-based vehicle identification approach. In this approach, comparatively high-mounted observation cameras were employed for collecting a roadway video feed; the Adjustable framework estimating, and the Gaussian shadowing reduction consisted of the two primary techniques used. The system's precision depends on the viewing angle and its capacity to eliminate shadowing and phantom effects.

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Streamlining Stock Price Analysis: HadoopEcosystem for Machine Learning Models and BigData Analytics

The rapid growth of data in various industries has led to the emergence of big data analytics as a vital component for extracting valuable insights and making informed decisions. However, analyzing such massive volumes of data poses significant challenges in terms of storage, processing, and analysis. In this context, the Hadoop ecosystem has gained substantial attention due to its ability to handle large-scale data processing and storage. Additionally, integrating machine learning models within this ecosystem allows for advanced analytics and predictive modeling. This article explores the potential of leveraging the Hadoop ecosystem to enhance big data analytics through the construction of machine learning models and the implementation of efficient data warehousing techniques. The proposed approach of optimizing stock price by constructing machine learning models and data warehousing empowers organizations to derive meaningful insights, optimize data processing, and make data-driven decisions efficiently. The proliferation of data has transformed the way organizations operate. The ability to extract valuable insights from vast amounts of data has become a competitive advantage across industries. However, traditional data processing and analysis techniques are insufficient to handle the sheer volume, velocity, and variety of big data.
This necessitates the adoption of advanced technologies and frameworks, such as the Hadoop ecosystem, to overcome these challenges. In recent years, the prevalence of big data technology has revolutionized numerous industries, including retail, manufacturing, healthcare, and finance.The utilization of big data has proven instrumental in enhancing operational efficiency by harnessing valuable insights derived from data analysis. This research paper focuses on investigating the application of big data analytics in the context of the stock market, utilizing a publicly available dataset sourced from The New York Stock Exchange (NYSE). By leveraging big data analysis, organizations can identify trends, patterns, and correlations that enable informed decision-making processes. Particularly in the stock market, analysis plays a pivotal role for investors and traders in assessing a company's intrinsic value before executing buying or selling decisions.
The widespread adoption and efficacy of big data technology is largely attributable to the evolution of multifarious frameworks and platforms that cater to the manipulation and scrutiny of colossal data sets. Apache Hadoop takes a preeminent position among these big data platforms, ingeniously amalgamating the powerful MapReduce paradigm and the durable Hadoop Distributed File System (HDFS) for proficient data governance. This technology has been embraced ubiquitously across a myriad of sectors, empowering organizations to distil pertinent insights, thus refining their decision-making apparatus. A case in point is the New York Stock Exchange (NYSE) that has judiciously harnessed big data technology, with a particular emphasis on Apache Hadoop, to conduct in-depth analysis of market fluctuations and draw data-oriented verdicts, conferring upon them a competitive superiority. In parallel, Apache Spark has emerged on the scene as a sought-after big data framework, renowned for its expedited processing velocity and its superior versatility in handling data, thereby outpacing the capabilities of its counterpart, Apache Hadoop.The New York Stock Exchange (NYSE) can harness the capabilities of Apache Hadoop’s MapReduce and Apache Spark frameworks to process and decipher vast quantities of financial data. As illustrated in Table 1, Spark offers superior processing speed and enhanced flexibility in data manipulation, rendering it a prime candidate for processing and analyzing real-time data pertinent to the financial sector, more specifically, within the ambit of stock exchanges. This proves particularly valuable in the dynamic realm of finance where instantaneous data insights are paramount to the decision-making process. In addition, Spark's fundamental component, the Resilient Distributed Dataset (RDD), presents an advantageous data processing approach within distributed systems, exhibiting higher efficiency and fault tolerance compared to MapReduce.RDD programming can be employed for data transformations, including mapping and filtering, as well as operations like counting and collecting. Given its ability to be cached in-memory, RDD enhances data access efficiency. Consequently, Spark can confer a competitive edge to stock exchanges, such as the NYSE, requiring the capability to process and dissect voluminous real-time financial data in order to maintain their standing in the brisk-paced financial industry.

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Support Vector Machine for Multiclass Classification of Redundant Instances

 In recent years, support vector machine has become one of the most important classification techniques in pattern recognition, machine learning, and data mining due to its superior classification effect and solid theoretical base. 

However, its training time will increase dramatically as the number of samples increases, and training will become more sophisticated when dealing with problems involving multiple classifications. A quick training data reduction approach MOIS appropriate for multi-classification tasks is presented as a solution for the aforementioned issues. While eliminating redundant training samples, the boundary samples that play a vital role are chosen in order to considerably reduce training data and the problem of unequal distribution between categories. 

The experimental results demonstrate that MOIS may maintain or even improve the classification performance of support vector machines while substantially enhancing training efficiency. On the Opt digit dataset, the suggested method improves classification accuracy from 98.94% to 99.05%, while training time is reduced to 15% of the original; in HCL2000, the proposed method improves classification accuracy from 98.94% to 99.05%. When the accuracy rate is marginally increased (from 99.29% to 99.30%) on the first 100 categories dataset, the training time is dramatically reduced to less than 6% of the original. Additionally, MOIS has a high operational efficiency.

 

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Cognitive Approach Using SFL Theory in Capturing Tacit Knowledge in Business Intelligence

The complexity of Business Intelligence (BI) processes need to be explored in order to ensure the BI system properly treats the tacit knowledge as part of the data source in the BI framework. Therefore, a new approach to handling tacit knowledge in the BI system still needs to be developed. The library is an ideal place to gather tacit knowledge. It is a place full of explicit knowledge stored in various bookshelves. Nevertheless, tacit knowledge is very abundant in the head of the librarians. The explicit knowledge they gained from education in the field of libraries and information was not sufficient to deal with a complex and contextual work environment. The complexity comes from many interconnected affairs that connect librarians with the surrounding environment such as supra-organizations, employees, the physical environment, and library users. This knowledge is contextual because there are various types of libraries and there are different types of library users who demand different management. Since tacit knowledge hard to capture, we need to use all possible sources of externalization of tacit knowledge. The effort to capture this knowledge is done through a social process where the transfer of knowledge takes place from an expert to an interviewer. For this reason, it is important for the interview process to be based on the SFL theory (Systemic Functional Linguistics).

The cognitive approach is ideally suited for capturing knowledge as from among the massive data available these days. The decision-maker typically must integrate multiple streams of information from the information or other collaboration with the knowledge systems in making decisions [1]. Furthermore, decisions may be based on organizational politics or routines [2], and decision-makers may limit themselves to a few choices because of “bounded rationality” [3]. Ducharme and Angelelli [4] invented the use of cognitive as advanced analytics to capture and extract tacit knowledge by elaborating the predictive analytics, stochastic analytics, and cognitive computing. Moreover, the advanced analytics approach still is implemented in the Business Intelligence (BI) environment [17]. Thus, the basic BI framework involving a tacit knowledge approach can be illustrated as shown in Figure 1.

There is a small number of earlier research about business intelligence in the academic library and library profession. An example of this research is Cox and Janti [5] on the Library Cube project, a business intelligence system that demonstrates the value that can be provided by academic libraries. However, the research is not targeting the tacit knowledge at all since it is only targeting the provided information in the academic information system. Heims et al [6] mention that reporting BI research and creating BI reports are the key area of responsibility of librarians in the information era. We addressed the problem by open dialog with the librarian, which actually what considered would happen between BI manager and librarian to develop clear communication channels [7]. Noted that for librarians, BI is part of their challenge in the information era [8].

Since tacit knowledge is hard to capture, we need to use all possible sources of externalization of tacit knowledge [9]. The effort to capture this knowledge is done through a social process where the transfer of knowledge takes place from an expert to an interviewer. For this reason, it is important for the interview process to be based on the SFL theory. 

According to SFL theory, only a fraction of “can-do” turned into “can mean” and only a fraction of “can mean” turned into “can say” [9]. This is what is meant by Polanyi when he said “we know more than we can tell” [10]. Hence, only a portion of tacit knowledge can be captured by linguistic means. We need other means that came up from “can mean” which anything that could analyze semiotically. It could be non-verbal cues or drawing, written text, etc.  We refer to drawing, photograph, videos, written text, and others as the documented sources and beyond our analysis. Here we just focused on non-verbal cues. However, whenever documented sources considered relevant, we could use it as a source of tacit knowledge.

A. Linguistic Source of Tacit

According to SFL theory, language is realized in four strata: semantic, lexicogrammar, phonology, and phonetics [11]. Semantics is the highest level that explains the hidden meaning of language. Lexicogrammar is an aspect of language that explains the real meaning, can be seen from the choice of words and grammar used. Phonology is the meaning that exists in sound. Phonetics is speech that arises from language activities. It can be seen that this stratification moves from something abstract (semantic) to something concrete (phonetic).

Someone will choose a word to represent his experience when speaking. What word or wording was chosen can distinguish whether the experience or knowledge expressed is an inheritance or not. In fact, sometimes, a person will find it difficult to find the right words to describe their knowledge so that they choose.

Even after knowledge has been expressed verbally and non-verbally, there is still space where the knowledge of tacit cannot be expressed at all and can only be demonstrated by behavior. Apart from observations requiring precise and specific time, experts generally do not like being observed while working [12]. In addition, observations become more complicated when several experts are involved [12]. This can only be done in a non-intrusive manner such as a surveillance camera, but it can be a problem with privacy issues. Alternatively, observations can be made through third-person testimonies. In this case, the interview was conducted on the third person who had witnessed the behavior of the first person who was the target to reveal the knowledge of his possessions.

The framework above shows the design used to capture the comprehensive knowledge of experts. Based on the SFL theory, tacit knowledge consists of three levels. The first level is the most basic level where a person can only do but cannot interpret it, let alone say it. This knowledge is contextual tacit knowledge because it can only be raised in a supportive context. It can only be collected through observation. Even so, because the context is very specific, in terms of space and time, only people present in that context can see and understand from their perspective what the tacit knowledge is. In this study, it is assumed that the person is a peer. Researchers collected data on tacit knowledge from peers through cognitive interviews. Furthermore, we can conclude there are two ways to collect tacit knowledge:

1. Focused on the stated problem. Participant presented with a problem which needs tacit knowledge to be solved. The tacit knowledge needed to solve this problem can be collected with interviews, based on respondents chosen with questionnaires. Questions in the interview informed by problems urgency, detected by questionnaire. Here, sequences of the steps determine the completeness of tacit knowledge. The figure below show the connection between questionnaire design and decision.

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