=Paper=
{{Paper
|id=Vol-3922/paper8
|storemode=property
|title=IoT-based Conversational Solar Vending Machine
|pdfUrl=https://ceur-ws.org/Vol-3922/paper8.pdf
|volume=Vol-3922
|authors=Dranoel Flores,John Jurwen Caoile,Marvin Andrielle Felipe,Criselle Centeno,Diony Abando,Richard Regala
|dblpUrl=https://dblp.org/rec/conf/iam/FloresCFCAR24
}}
==IoT-based Conversational Solar Vending Machine==
https://ceur-ws.org/Vol-3922/paper8.pdf
IoT-based Conversational Solar Vending Machine
Dranoel Flores1,∗,† , John Jurwen Caoile1 , Marvin Andrielle Felipe1 , Criselle Centeno1 ,
Diony Abando1 and Richard Regala1
1
Pamantasan ng Lungsod ng Maynila, Gen. Luna Cor. Muralla Sts., Intramuros, Manila, Philippines
Abstract
In vending machine retail settings, there are major challenges it encounters: lack of real-time monitoring
capabilities, continuous electricity consumption, and a lack of human interaction. To address this, the researchers
developed an IoT-based vending machine with solar panels and conversational AI assistance that allows real-time
monitoring of stocks of the vending machine that allows the operator to manage the vending machine online and
even gets notified once a certain product is almost out of stock, solar panels and voice recognition command
are also implemented. By implementing these, the systems utilize a more sustainable alternative energy source
as opposed to traditional energy sources and also provides a way for the customer to interact with the vending
machine system. The IoT was evaluated with the ISO/IEC 25010:2023 standards, focusing on Functional Suitability,
Performance Efficiency, Interaction Capability, Reliability, and Maintainability. The results show a favorable
score of 4.86 in Functional Suitability, 4.86 in Performance Efficiency, 4.95 in Interaction Capability, 4.83 in
Reliability, and 4.70 in Maintainability. These results show that the IoT developed for monitoring the vending
machine was dependable and highly favorable. By developing an IoT-based vending machine with solar panels
and conversational AI assistance, the system provided a significant advancement in automated retail settings.
Keywords
Conversational AI, Internet of Things, Solar Panel, Vending Machines, Voice-activation
1. Introduction
Vending machines on their own are already a state-of-the art idea in retail automation. These kiosks
are self-service and placed in key spots that provide seamless shopping experience. In addition to their
quick and simple transactions, they also offer a variety of options, from tasty snacks and cool drinks to
necessary personal hygiene products, so every customer can find something that suits their particular
need on the go. For vending machine operators, they need to manually check the system from time to
time to see if inventory’s doing good. Internet of Things could address this challenge by implementing
a seamless connection between the vending machine and humans [1, 2]. With the introduction of the
Internet of Things, it will help the vending machine operators to have real-time monitoring capability.
They will be notified once a slot in the vending machine is almost out of stock, they can also modify the
information and inventory of the products the vending machine contains. This will help the vending
machine operator to keep a track on when to restock the products. In addition to this, the operator will
be able to see which products are the best-sellers of a certain timeframe. This will allow them to have
an insight into which products should be on the vending machine more often and how frequently it
needs restocking.
While vending machines provide convenience in retail settings, one main problem with this is its
substantial electricity consumption linked to their continuous operation. Given this difficulty, vending
machines can introduce and adopt creative ways to address and solve this issue to not only transform
energy footprint of the industry, but to also reduce ends. Instead of paying electricity bills, this payment
could be saved as a profit. This shows room for improvement and innovation to find sustainable
alternatives to electricity consumption. Looking into renewable energy as an alternative energy source
could provide a way to lessen environmental impact and cut carbon emissions– to encourage a more
7th International Conference on Informatics and Applied Mathematics IAM’24, December 4-5, 2024, Guelma, Algeria
∗
Corresponding author.
†
These authors contributed equally.
Envelope-Open drflores2020@plm.edu.ph (D. Flores)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
sustainable and environmentally friendly practice. As an example, the use of solar electricity and panels
can provide a sustainable and eco-friendly way to address the issues with vending machine electricity
consumption [3, 4]. Solar power is a flexible energy source that can also be utilized for energy storage,
excess solar energy generated during the hours of maximum sunlight can be saved for use at a later
time.
In addition to this, due to the simplistic nature of vending machines, it needs little to no human inter-
actions. Conversational AI systems may provide precise, timely, and contextually relevant information
that improves user experience and encourages well-informed decision-making by utilizing the power
of AI-driven analytics and data processing [5, 6]. The swift growth of conversational AI is indicative of
its significant influence in transforming the digital communication and engagement scene, paving the
way for improved interaction and information sharing in the artificial intelligence space. This could
provide a new level of engagement and interaction with the customers in automated retail settings.
2. Related Work
IoT has rapidly emerged as a versatile technology with broad applications across various industries,
significantly influencing numerous sectors. Ranging from wearable gadgets and smart vehicles to
industrial use cases, IoT has introduced a new era of interconnected devices, enabling seamless inter-
action between machines and humans previously only seen in futuristic movies. The paper explored
real-world examples of how retail brands have integrated IoT technology and the resulting opportunities
for retailers [1].
Retailers are actively exploring novel ways to engage with customers and various channels of
interaction. Forecasts indicate that the Internet will play an increasingly pivotal role in everyday life,
leading to a future where inanimate objects are interconnected and possess computing capabilities,
giving rise to the concept of ’smart objects.’ Internet of Things serves as a comprehensive tool for
fostering innovation in service-centric retail industries. IoT presents a novel approach that elevates the
traditional shopping experience by making it more convenient, enjoyable, interactive, and personalized,
thereby revolutionizing the retail landscape [2].
Solar energy is harnessed by converting sunlight into electricity using solar panels. These panels
absorb solar thermal energy and convert it into electrical energy, which can then be stored in batteries
or converted to alternating current (AC) using an inverter. Excess electricity can be stored for later use,
making solar power a reliable energy source. Solar energy systems can be passive or active. Passive
systems do not require special tools and can be as simple as parking a vehicle in the sun to absorb
heat. Active systems, on the other hand, require specific methods and resources to collect and store
energy efficiently. Both solar thermal and photovoltaic plants use an active solar energy system. The
application of solar power plants, especially rooftop solar power plants, is gaining popularity in urban
areas. Rooftops of buildings can be utilized to collect and store solar energy, contributing to energy
security and promoting the use of renewable energy sources. The rooftop solar power plant has become
a national priority program, supported the goal of energy distribution and promoted the use of new
and renewable energy sources [7].
Rommila et al. developed an interactive artificial intelligence agent using deep learning techniques
like Long Short-Term Memory, Gated Recurrent Units, and Convolution Neural Networks to predict an
appropriate and automatic response to customers’ queries. This allowed for the automatic generation
of conversation between a computer and a human. They found many examples of users feeling that the
chatbot offers companionship and emotional support, and that it is understanding, approachable, and
capable of meeting users’ communication needs [8].
Particularly in the field of information and communication technology (ICT), the pandemic has
increased the demand for creative online learning solutions. Using chatbot technology to improve
learning experiences is one suggested remedy. Deeper comprehension of the course material is made
possible by this integration, which also promotes active engagement in real-world situations and
considers different learning preferences and intelligences. The positive impact of chatbot technology
Figure 1: Theoretical Framework
on learners’ academic performance, demonstrating significant improvements in scores before and
after chatbot implementation. Chatbots offer personalized support, helping to clarify and expand
subject knowledge through interactive content like texts, graphics, audio, and videos. Chatbots and
other conversational AI are essential for increasing client satisfaction, decision-making processes,
and engagement. Chatbots may effectively fulfill consumer requirements and preferences by offering
personalized, interactive experiences [9].
3. Methodology
3.1. Theoretical Framework
Business Intelligence Systems explores the idea of implementing Internet of Things to physical things
and other electronic appliances for the purpose of enabling connection and exchange of data. This data
can then be used to retain businesses for an analytical view for better decision making, thus enhancing
customer experience and increasing their sales (Ved, 2020). IoT services provide new and innovative
approaches to the existing services, and this significantly increases the positive impact on customers’
satisfaction. Not only that, but this implementation also helps the employees in the retail sector to ease
inventory services [10].
With this study’s background and Figure 1, the IoT-based vending machine with solar panel and
Conversational AI assistance aligns with this method focusing on easing inventory services while also
promoting user engagement and satisfaction. The vending machine system uses IoT functionality to
provide real-time monitoring services to the operator. It also implements Conversational AI assistance
to allow user engagement, improve experience, and even promote sales.
To integrate conversational AI into the vending machine, a voice is first input from a microphone
which is then sent to Google Speech-to-Text (STT) which transcribes the audio to text. Dialogflow then
analyzes the text to extract the user’s intent or command. Based on this understanding, the vending
machine then takes the appropriate action. For example, if the user requests a product, Dialogflow
triggers the action to check if the product is available in the vending machine. Once the action is
performed, a pre-programmed response is played through the vending machine’s speaker. Google
Text-to-Speech (TTS) system is used to convert the response back into speech for confirmation of the
process. This process ensures smooth interaction with the vending machine entirely through voice,
creating a hands-free user experience.
In order to train the Natural Language Understanding (NLU) Dialogflow, the developers first defined
the core intents and entities on possible queries and actions the user might have when interacting
with the vending machine system like ordering, checking availability, making payment, or asking for
assistance. The developers collected phrases like “Order Rebisco” and In order to train the Natural
Figure 2: Conceptual Framework
Language Understanding (NLU) Dialogflow, the developers first defined the core intents and entities on
possible queries and actions the user might have when interacting with the vending machine system
like ordering, checking availability, making payment, or asking for assistance. The developers collected
phrases like “Order Rebisco” and “Order Combi” to teach Dialogflow to recognize different variations
of similar commands. Once the intents and entities are defined with relevant training phrases, the
developers were able to configure responses that the vending machine will deliver based on the user
input.
3.2. Conceptual Framework
Figure 2 shows the conceptual framework of the system proposed. It shows the Internet of Things
implementation to the vending machine, it is connection to Firebase for the real-time monitoring
capabilities for its inventory. In addition, the solar panel integrated is an added alternative to the
conventional power sources, allowing sustainable and renewable consumption. The user may interact
with the vending machine and the vending machine operator can access the mobile application to
monitor its activities.
3.3. System Architecture
The proposed system’s physical components are depicted in the figure 3. This illustration helps to
better understand the connection of each component and how they will function in order to execute
the proposed system. The Raspberry Pi acts as the microcontroller that controls and commands each
component. The 12V solar cell is connected to a solar charge controller that regulates solar energy.
The battery can also be charged either through AC by plugging it in an outlet or through the solar
charger. Between all the components and the battery, a relay channel is placed to allow the operator to
use solar energy directly or the battery as a power source. The PIR Sensor will trigger the initialization
of the system, when motion is detected, the transaction starts. The 360-degree continuous rotation
servo motors rotate the string thus pushing the item. A bill and coin acceptor are also integrated for
the payment of items. The keyboard matrix is one medium where the user can input the choice of their
preference.
Figure 3: System Architecture
3.4. System Prototype Implementation
This figure 4 shows the prototype implemented by the developers to showcase and demonstrate
the functionality and efficiency of an IoT-based vending machine using solar-powered energy. This
integration ensures eco-friendly and sustainable power usage, as an alternative to traditional power
sources. In addition, the vending machine system is equipped with Conversational AI assistance
allowing user interactions through voice commands for a seamless and hands-free experience. This
mix of IoT technology, renewable energy, and AI-driven interaction demonstrates automated vending
systems’ future potential for boosting consumer convenience while reducing energy use.
3.5. Mobile Application
This figure 5 shows the dashboard of the mobile application developed. This shows the overall analysis
of the consumption of the IoT-based vending machine. It shows two graphs: quantity of sales and
revenue for the current week. It shows the number of sales made per day along with its revenue. This
could provide the vending machine operators with a summary of the usage of the system: how much
they earned and the frequency of sales.
This figure 6 shows the Inventory Tab of the application. This provides real-time monitoring capability
for the vending machine operator. It gets updated every purchase made by the consumer. The operator
can also edit out the inventory and the information of the product in each slot. In addition, the operator
will be notified once a product is almost out of stock. This will help them be updated to the inventory
of the vending machine and they can refill it even before it fully runs out of stock.
In the Notifications Tab, the vending machine operator will be able to see all the notifications they
Figure 4: System Prototype Implementation
Table 1
Five-point Likert Scale
Criteria Numerical Value
Excellent 4.21 - 5.00
Very Good 3.41 - 4.20
Good 2.61 - 3.40
Fair 1.81 - 2.60
Poor 1.00 - 1.80
received based on the transaction with the system. They will be notified if a product is almost out of
stock, if it’s out of stock, and if they were able to refill or update the product inventory.
The figure 8 shows information about the user. The Account Tab also contains the Settings Tab
wherein the user can configure the Wi-Fi connection of the vending machine system, add products for
future references, log out of their account, and delete their account.
3.6. System Evaluation
To assess the system’s performance, 100 respondents were asked to answer a survey based on ISO
25010:2023 standard. The assessment form provided allowed respondents to gauge the overall impact
and effectiveness of the system. The effectiveness of the system was evaluated using a five-point Likert
scale with the focus on Functional Suitability, Performance Efficiency, Interaction Capability, Reliability,
and Maintainability shown in Table 1:
1 Functional Suitability: Evaluated the system’s ability to provide real-time monitoring capabilities
to the vending machine, utilize solar panels as an alternative energy source, and conversational
AI to provide user engagement.
2 Performance Efficiency: Evaluated the system’s ability to perform its function and efficiency in
the use of its resources under specified conditions.
3 Interaction Capability: Evaluated the system’s ability to be interactable and complete specific
tasks based on the conversational AI.
Figure 5: Dashboard Tab
Table 2
Population and Sample
Criteria Numerical Value Percentage Sampling Method
Vending Machine Users 50 90.91% Purposive
Poor 5 9.09% Sampling
4 Reliability: Evaluated the system’s ability to perform its functionality under specified conditions
for a specified period of time.
5 Maintainability: Evaluated the system’s effectiveness and efficiency to be modified and adapted
to changes with little to no compromise.
The target population of the system had experience in using vending machines or at least familiar
with the concept of who can provide observations for the needed information for the study. To ensure
a sufficient sample size. The developers target at least fifty vending machine consumers and five IT
professionals. In addition, purposive sampling, a non-probability technique, was used to select vending
machine users based on specific criteria that are relevant to the research objectives. This focus and
relevant sample are expected to provide valuable insights into meaningful conclusions.
In order to examine the data gathered by the developers, the following statistical tools were used in
Figure 6: Inventory Tab
this study. The developers will use the following statistical treatments for this study:
1 Percentage: used to get the percentage of the distribution of respondents in each of the categories
considered. This was used to show the profile of the data gathered from the respondents.
Formula:
𝑓
𝑃 = ( ) × 100%
𝑛
Where:
– P = Percentage (in %)
– f = Frequency of each group of respondents in the sample size
– n = Total sample of respondents
2 Mean: used to find the average value of a specific variable in the data. It allows developers to
understand the typical value of the parameter being studied.
Formula:
∑𝑥
𝑥̄ =
𝑛
Where:
– 𝑥 ̄ = Sample Mean
– x = Frequency of each group of respondents in the sample size n
Figure 7: Notifications Tab
– n = Total sample of respondents
3 Mode: the value that has a higher frequency in a given set of values. It is the value that appears
to be the greatest number of times.
4. Results and Discussion
The results are shown in tabular form, with the average score for each goal and its subcategories accord-
ing to the ISO/IEC 25010:2023 framework. Functional Suitability, Performance Efficiency, Interaction
Capability, Reliability, and Maintainability are some of these categories.
Figure 8: Account Tab
4.1. Evaluation Results
The ISO/IEC standards offer a framework for assessing software quality, which were implemented in
this study. This model contains ten quality attributes in total that can be used to evaluate the overall
software quality. The following data were obtained from an ISO 25010:2023 analysis of the paper ”An
Internet of Things Based - Revolutionizing Vending Machine System with Solar Panel and Conversa-
tional AI Assistance.” With the score of 4.86 for Functional Suitability, a score of 4.86 for Performance
Efficiency, 4.95 for Interaction Capability, 4.83 for Reliability, and 4.70 for Maintainability; the system
has demonstrated its effectiveness in meeting the users’ needs and expectations and interpreted as
Excellent.
Table 3
Summary of System Evaluation Results
Criteria Numerical Value Interpretation
Functional Suitability 4.86 Excellent
Performance Efficiency 4.86 Excellent
Interaction Capability 4.95 Excellent
Reliability 4.83 Excellent
Maintainability 4.70 Excellent
Overall Mean 4.84 Excellent
Table 4
Functional Suitability Testing
Criteria Numerical Value Interpretation
Functional Completeness 4.74 Excellent
Functional Appropriateness 4.98 Excellent
Table 5
Performance Efficiency Testing
Criteria Numerical Value Interpretation
Time Behavior 4.86 Excellent
Table 6
Interaction Capability Testing
Criteria Numerical Value Interpretation
Operability 4.92 Excellent
User Engagement 4.98 Excellent
4.2. System Evaluation
The table below shows the numerical summary of the evaluation results for the system according
to the ISO 25010:2023 as evaluated by the respondents. The overall mean of 4.84 underscores the
system’s exceptional satisfaction, asserting its suitability, efficiency, interaction capability, reliability,
and maintainability. This feedback from respondents hence indicates the system’s success in fulfilling
its objectives.
The table above shows the numerical summary of the evaluation results for the system according
to the ISO 25010:2023 as evaluated by the respondents. The overall mean of 4.84 underscores the
system’s exceptional satisfaction, asserting its suitability, efficiency, interaction capability, reliability,
and maintainability. This feedback from respondents hence indicates the system’s success in fulfilling
its objectives.
Table IV above shows the summary of the evaluation results for the system in terms of Functional
Suitability. Functional Completeness shows a score of 4.74 while Functional Appropriateness gained a
score of 4.98, both indicating an Excellent score.
Table V above shows the summary of the evaluation results for the system in terms of Performance
Efficiency. Time Behavior shows a score of 4.86 indicating an Excellent score.
Table VI above shows the summary of the evaluation results for the system in terms of Interaction
Capability. Operability shows a score of 4.92 while User Engagement gained a score of 4.98, both
indicating an Excellent score.
Table VII above shows the summary of the evaluation results for the system in terms of Reliability.
Faultlessness shows a score of 4.84 while Availability gained a score of 4.82, both indicating an Excellent
Table 7
Reliability Testing
Criteria Numerical Value Interpretation
Faultlessness 4.84 Excellent
Availability 4.82 Excellent
Table 8
Maintainability Testing
Criteria Numerical Value Interpretation
Modularity 4.74 Excellent
Modifiability 4.66 Excellent
Table 9
Solar Panel Reliability Testing
Day Weather Con- Solar Power Observation
dition Duration
(Hours)
Day 1 Sunny 10 Optimal charging and powering conditions
with direct sunlight.
Day 2 Cloudy 5 Reduced sunlight led to shorter powering
time.
Day 3 Cloudy 6.2 Similar conditions to the previous day.
Day 4 Sunny 11.2 Direct sunlight resulted in efficient charg-
ing and powering of the machine.
Day 5 Partly Cloudy 7.1 Some interruptions in sunlight caused
moderate delays.
Day 6 Partly Cloudy 4.2 Rain and intermittent clouds impacted
and Brief Rain charging.
Day 7 Mostly 3.9 Limited sunlight led to the shortest opera-
Cloudy tion of the week.
score.
Table VIII above shows the summary of the evaluation results for the system in terms of Maintain-
ability. Modularity shows a score of 4.74 while Modifiability gained a score of 4.66, both indicating an
Excellent score.
4.3. Solar Panel Reliability
To test the capability and reliability of the solar panel to power the vending machine prototype as an
alternative power source instead of traditional power sources, the researchers charged the vending
machine numerous times and recorded the performance metrics during each cycle. These measures
were then contrasted with the vending machine’s specifications. The data that was gathered provided
insight into the viability of using solar energy as a substitute for powering vending machines.
The table above shows how long the vending machine can be sustained by the solar panel only. On
days with optimal sunlight, the vending machine was powered for longer time frame, demonstrating
the solar panel’s efficiency under ideal conditions. The impact of weather fluctuation on the charging
process was also demonstrated by the considerable decrease in powering the machine during cloudy
days or times with less sunlight. This table illustrates the dependency of the charging time on weather
conditions, emphasizing the importance of stable and ample sunlight for efficient operation of the
solar-powered vending machine. It is important to note that this data was gathered from the vending
machine prototype. In a larger machine, which requires more power to operate, a bigger and more
robust solar panel would be necessary to meet the increased power requirements for both machines
and their battery. In addition, charging time is influenced by the weather and environmental conditions
such as temperature and physical placement of the vending machine. If the vending machine is placed
in an area with consistent exposure to direct sunlight it would perform better compared to one located
in a shaded or less sunny area.
5. Conclusions and Recommendations
This section delves into the research’s findings, draws conclusions based on the results and discussions,
and provides recommendations for further enhancing the system. In addition, suggestions for system
improvement and enhancement will be made in order to direct the upcoming direction and maximize the
usefulness of the system. By taking a comprehensive approach, we hope to offer clear comprehension
of the research findings and open the door for strategic improvements in the effectiveness of the system.
5.1. Conclusions
With the system evaluation and in alignment with the project’s objectives, the developers reached the
following conclusive findings:
1 The development of the mobile application as a platform to monitor the vending machine proved
as an effective way to remotely gain access to check its status. Showcasing the vending machine’s
inventory also provided the vending machine’s operator about the activity of the vending machine:
sale, revenue, and inventory. This advancement contributes to a smarter and efficient academic
technology beneficial to vending machine operators.
2 Through the study, the developers successfully addressed the continuous electricity consumption
of vending machines. The vending machine was able to utilize solar cells as an alternative to
traditional power source while at the same time being more sustainable. This part of the study
highlights the importance of using renewable energy sources as an alternative.
3 User engagement and involvement were greatly increased by the usage of Conversational AI
support. Voice commands enabled conversational communication between users and the system,
enabling real-time interactions and smooth platform navigation, resulting in an enhanced and
user-friendly experience.
5.2. Recommendations
Based on the analysis of the study’s findings, the developers offer the following recommendations to
further improve the Internet of Things Based - Revolutionizing Vending Machine System with Solar
Panel and Conversational AI Assistance:
1 To enhance the availability of the system, consider implementing more products in the vending
machine. This will allow consumers to have more options to choose from when ordering. In
addition, the system can also have a cooling system to introduce more items like soft drinks.
2 Future researchers may also implement a bigger solar cell for bigger system or introduce other
renewable energy as an alternative to traditional power generation.
3 To broaden the scope of Conversational AI, it is recommended that future researchers focus
on enhancing its capabilities beyond simple command recognition, exploring more advanced
interactions and functionalities.
Acknowledgments
The researchers extend their heartfelt sincerity and boundless gratitude to the individuals who played
an important role in bringing their research to fulfillment. To Prof. Ariel Antwaun Rolando C. Sison,
the esteemed chairperson of the College of Information System and Technology Management, whose
leadership and wise feedback played a vital role in shaping the subject of our project, and to Prof.
Criselle J. Centeno, our thesis adviser, Prof. Diony S. Abando and Prof. Richard C. Regala, our panelists,
we express our heartfelt appreciation. Your knowledgeable critique and invaluable suggestions have
significantly improved our work. In the spirit of companionship, the developers also want to extend their
warm appreciation to their fellow researchers. It is through their collaboration that an environment of
collective growth and accomplishment was fostered. The developers are also profoundly grateful to
their family and friends, including the College of Information System and Technology Management.
Their enduring patience and unwavering support have served as the cornerstone of the developers’
perseverance. Above all, the developers’ hearts are filled with gratitude to the almighty God for the
boundless blessings and inspiration have been the driving force behind their endeavors.
Declaration on Generative AI
The authors have not employed any Generative AI tools.
References
[1] R. G. Kargal, R. Suresh, Internet-of-things (iot) in the retail industry: Consumer perceptions and
key considerations for retail adoptio, AAYAM: AKGIM Journal of Management 10 (2020) 9–13.
[2] A. A. Malysheva, B. Rawat, N. Singh, P. C. Jena, K. Kapil, Energy efficiency assessment in smart
homes: A comparative study of energy efficiency tests, in: BIO Web of Conferences, volume 86,
EDP Sciences, 2024, p. 01083.
[3] U. Mohan, P. Singh, K. M. Agarwal, S. K. Sharma, A. Yadav, C. Sharma, Solar power generation,
utilization and management for facilitating sustainable development in india, in: IOP Conference
Series: Materials Science and Engineering, volume 1168, IOP Publishing, 2021, p. 012004.
[4] H. H. Pourasl, R. V. Barenji, V. M. Khojastehnezhad, Solar energy status in the world: A compre-
hensive review, Energy Reports 10 (2023) 3474–3493.
[5] A. V. Prakash, S. Das, Intelligent conversational agents in mental healthcare services: a thematic
analysis of user perceptions, Pacific Asia Journal of the Association for Information Systems 12
(2020) 1.
[6] D. Flores, J. J. Caoile, M. A. Felipe, C. Centeno, D. Abando, R. Regala, An internet of things
based-revolutionizing vending machine system with solar panel and conversational ai assistance,
Available at SSRN 5001048 (2024).
[7] H. Ummah, R. Setiati, Y. Dadi, M. Ariq, M. Malinda, Solar energy as natural resource utilization
in urban areas: Solar energy efficiency literature review, in: IOP Conference Series: Earth and
Environmental Science, volume 780, IOP Publishing, 2021, p. 012007.
[8] P. M. Romilla, J. Matuguinas, A. J. Santiago, D. M. Cortez, C. Centeno, A. A. R. Sison, M. A. Mercado,
Children in-conflict chatbot system using natural language processing technique, World Journal
of Advanced Research and Reviews 18 (2023) 425–429.
[9] R. R. Resabal, Chatbot learning integration: Learners’ performance enhancement tool, Psychology
and Education: A Multidisciplinary Journal 16 (2024) 1027–1033.
[10] V. V. Ratna, Conceptualizing internet of things (iot) model for improving customer experience in
the retail industry, International Journal of Management 11 (2020).