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This study investigates the role of human intervention in artificial intelligence/machine learning (AIML)-driven predictions. By doing so, we distinguish between three different types of human-AIML collaboration: automation, adjustable automation, and augmentation. We theorize that prediction uncertainty and time horizon represent two critical determinants of forecast accuracy. Based on a field experiment involving AIML-driven demand forecasts approximately 1,888 stock-keeping units in the retail industry, we rely on a multivalued treatment effect methodology to measure the effects of human-AIML collaboration on forecast accuracy. Our findings show that human intervention complements AIML-driven forecasts most effectively (augmentation) in predictions with long time horizons and low uncertainty. However human intervention is least likely to contribute to the effectiveness of AIML predictions (automation) in environments with short time horizons and high uncertainty. We discuss implications for extant theory and propose a framework outlining the conditions in which human intervention is most likely to add predictive value to human-AIML collaborations. [ABSTRACT FROM AUTHOR]

The field of chatbots has witnessed a remarkable evolution in recent years, marked by a transition from simplistic rule-based structures to sophisticated systems employing advanced natural language processing (NLP) techniques. While most languages benefit from NLP support, the majority of chatbot research and development has been conducted in English, leaving a notable scarcity of comparable works in Arabic. This scarcity is attributed to the myriad challenges posed by the linguistically intricate nature of Arabic, encompassing orthographic variations and diverse dialects. This study systematically reviews articles that represent implementations of Arabic chatbots, revealing a discernible shift from rule-based frameworks to the predominant adoption of machine learning (ML) and deep learning (DL) methods. The results highlight the dynamic trajectory of chatbot technology, with a notable emphasis on the pivotal role of DL, as evidenced by a significant peak in 2023. Looking forward, the study anticipates a more sophisticated future for chatbot development, driven by ongoing advancements in artificial intelligence (AI) and NLP, offering valuable insights into the current state of Arabic chatbot research and laying the foundation for continued exploration in this evolving and dynamic field. [ABSTRACT FROM AUTHOR]

In this research article, authors have engineered the model to control soft material robotic car with single and multi-hand gestures based on Artificial Intelligence and Machine Learning approach. A user centric representation of this features is obtained by using hand detection and hand gesture recognition mechanism. Authors have assigned the desire hand gestures to move the soft material robotic car in 360 degrees. In this research work, different gestures of human hands are simulated in terms of landmarks using Mediapipe. Each combination of hand landmark performs different actions to control the robotic car in multiple directions. In this research work, the soft material robotic car has been controlled using single hand as well as multi hands gesture-based approach. Our motive of this research work is to reduce human efforts by controlling the soft material robotic car through hand gestures based on AIML techniques. The proposed CNN model has yielded high accuracy as compared to baseline ML algorithms such as KNN, Decision Tree, SVM etc. It is also observed that CNN model remains ahead of other ML algorithms for both single hand and multi-hand gesture based robotic car control. The performance of proposed CNN model for controlling robotic car has been represented in term of ROC curve using validation and test data set. The average performances of single hand gesture and multi hand gestures using proposed CNN are found as 97% and 98% respectively. [ABSTRACT FROM AUTHOR]

Printed Circuit Board (PCB) serves as the fundamental backbone of the modern electronics industry, providing essential support and connectivity for a wide range of electronic components within electronic devices. This research paper provides an overview of PCB defect detection techniques and different categories of defects. Various defects have been identified using current methods; however, certain defects persist undetected. This paper offers insight that can propel researchers towards innovations in this domain, coupled with a thorough exploration of potential enhancements. A total of twelve papers have been taken into account, and this survey examines the papers spanning from the years 2019 to 2023. The goal is to provide a comprehensive review of cutting-edge techniques, the models used, their accuracies, defects detected, and improvements, intending to advance the field of PCB defect detection. It also outlines potential avenues for further research, making it a valuable resource for researchers seeking to enhance this field in the future. The survey encompasses diverse detection techniques, which include algorithms based on the CNN, MobileNet, YOLO, SSD, and FPN models, etc. By comparing their accuracy, researchers can gain valuable insights into the effectiveness of each approach for different types of PCB defects. The paper also addresses the limitations of existing methods, identifying areas for improvement and future scope. In conclusion, this study is a valuable resource for researchers, engineers, and business professionals involved in the fields of electronics manufacturing and quality control. By consolidating a diverse range of techniques and insights, it paves the way for the development of more sophisticated, accurate, and cost-effective PCB defect detection techniques that guarantee the performance and dependability of electrical equipment in all industries. [ABSTRACT FROM AUTHOR]

The aim of this study is to build an artificial intelligence chatbot application to support public health services. The chatbot acts as an information service that can replace the role of humans. The analysis of functional needs was obtained from information submitted by one of the heads of public health centers in Indonesia. This study uses the Scrum method with pregame stages to produce a plan consisting of functional and non-functional requirements analysis and conceptual design of the chatbot, which will be developed using Unified Modeling Language (UML) diagrams. The process of finding answers uses the matching graph master technique, which is a backtrack matching that utilizes a depth-first search strategy. There are 6 topics of chatbot services, including service schedules, health information, registration, diseases, drugs, and early care services for chatbot users. Tests conducted on these 6 topics showed an average correct answer ratio of 93.1% out of a total of 251 questions. The result of the usability measurement on the chatbot application that has been built obtained a system usability scale value of 80.1, indicating that the developed chatbots are acceptable for use. [ABSTRACT FROM AUTHOR]

Artificial Intelligence has evolved in sophistication and widespread use. This study aims to create a chatbot application in the health sector regarding the early diagnosis of pregnancy disorders. Based on basic health research, only 44 percent of pregnant women know the danger signs of pregnancy. The chatbot application developed is expected to facilitate and increase knowledge for pregnant women about the danger signs of pregnancy, especially early diagnosis of pregnancy disorders. The chatbot application was developed with artificial intelligence technology based on Artificial Intelligence Markup Language with the question-answer concept using the Pandorabots framework. The test is carried out in two stages: functional and pattern matching. The functional testing uses the black-box testing method, and the pattern-matching test on the chatbot uses the sentence similarity and bigram methods based on user input and keywords similarity in the bot's knowledge base. The functional testing results show that the chatbot application runs well, with the eligibility criteria reaching 81.4% and the results of the keyword similarity test (pattern matching) are zero to one, in the sense that the value of one has the same similarity between user input and pattern. Meanwhile, the zero value has no similarities, so the bot will respond to it as free input. So it can be concluded that the bot can respond to user questions when the pattern and input have the same level of similarity.

The creation of automatic e-mail responder systems with human-quality responses is challenging due to the ambiguity of meanings and difficulty in response modeling. In this paper, we present the Personal Email Responder (PER); a novel system for email categorization and semi-automatic response generation. The key novelty presented in this paper is an approach to email categorization that distinguishes query and non-query email messages using Natural Language Processing (NLP) and Neural Network (NN) methods. The second novelty is the use of Artificial Intelligence Markup Language (AIML)- based chatbot for semiautomatic response creation. The proposed methodology was implemented as a prototype mobile application, which was then used to conduct an experiment. Email messages logs collected in the experimental phase are used to evaluate the proposed methodology and estimate the accuracy of the presented system for email categorization and semi-automatic response generation. [ABSTRACT FROM AUTHOR]

For the development of chatterbots, the AIML idea is used. Because the AIML uses a theoretical framework that is syntactic and semantic in structure. The main concepts in the Pattern Recognition area are given in order to achieve this. The AIML language is then explained, with each AIML command/tag being followed by an application tag. Pattern Recognition is one of the theories and technologies used to construct chatterbots. Pattern Recognition is based on representative stimulus-response type blocks, into which the user enters a sentence and the software generates a result based on that input. The discourse continues in this sequence. Chatterbots are written in the AIML programming language. The Pattern Recognition approach is used by the chatterbot. [ABSTRACT FROM AUTHOR]

Saat ini sebagian besar universitas menggunakan sistem informasi web untuk menyampaikan informasi terkait informasi pendaftaran mahasiswa, akademik, beasiswa, biaya pendidikan dan lain-lain. Dalam hal pelayanan pendidikan, tentunya universitas perlu memberikan layanan yang terbaik, agar para civitas akademik kampus, masyarakat mendapatkan kepuasan terhadap layanan yang diberikan. Penelitian ini adalah mengembangkan aplikasi chatbot yang dapat digunakan sebagai layanan informasi kampus dan akademik bagi masyarakat umum maupun bagi civitas akademik kampus Universitas Lancang Kuning Adapun tahapan dalam pengembangan aplikasi chatbot ini diantaranya adalah pengumpulan kebutuhan, desain, membuat prototype, evaluasi dan perbaikan. Adapun metode yang digunakan untuk pembelajaran chatbot menggunakan Artificial Markup Language (AIML). Knowledge dari aplikasi chatbot ini adalah alamat kampus, syarat pendaftaran, langkah pendaftaran, program studi, jalur kuliah, berapa biasa kuliah dan cara daftar. Berdasarkan hasil pengujian yang telah dilakukan dengan metode whitebox dan blackbox, bahwa aplikasi chatbot dapat berjalan dengan baik sebesar 100%. Sedangkan pengujian menggunakan UAT sebesar 95%. Hal tersebut menunjukkan bahwa aplikasi chatbot mampu menjawab pertanyaan-pertanyaan yang diajukan, sesuai dengan pengetahuan yang telah diberikan sebelumnya. . Kata kunci: Akademik, AIML, Chatbot. Abstract Currently, most universities use web-based information systems to provide information on student enrollment, academics, scholarships, fees, and so on. As far as education services are concerned, of course, universities need to offer the best service to ensure that the campus learning community is happy with the services offered. The goal of this research is to build a chatbot application that can use as a campus and academic information service for the general public and the academic community of Universitas Lancang Kuning. The phases in the development of this chatbot framework include gathering needs, designing, creating prototypes, testing and improving. The tool used to learn the chatbot uses the Artificial Markup Language (AIML). Knowledge from the chatbot application is the campus address, registration requirements, registration steps, study program, course path, how many lectures are used and how to register. Based on the results of the tests carried out using the Whitebox and BlackBox methods, the chatbot framework will run 100% . While testing using UAT is 95%. The Chatbot framework can answer the questions that have been posed, based on the information that has been previously given. Keywords: Academic, AIML, Chatbot

A Chatbot is a software application. It is the real time chat between a computer and a human. It requires continuous testing. Smart bot interacts with users in their Natural language. The bot can be able to detect emotions on the basis of words used. Chatbot are currently gaining a lot of popularity. As they have automate customer service and reduce human efforts. Its aim is the communication with the user with the help of AIML files and Natural Language Processing (NLP). All Chatbot have two functions, namely understanding and answering. Chatbot's are of high significance as they are high automated programs that interact with humans just like a human would do. They are cost effective. They interact with human without any time bound and location bound. In this Chatbot we will be making a Chatbot which can express emotions just like humans. It would be human machine conversation and interactions. In order to remove the complexity Frequently Asked Questions (FAQ) bots will be made. They provide a user interface which allows users to type commands and receive instant replays. [ABSTRACT FROM AUTHOR]

Nowadays, machine-based conversational systems known as chatbots have emerged for many applications in our lives. Chatbots are intelligent tools that use the concept of Artificial Intelligence (AI) in order to communicate with humans using their natural languages. The inherent role of chatbots is to understand users' questions and provide the most appropriate responses intelligently and naturally. Chatbots have been very successful in some of the most spoken languages; however, Arabic chatbots have not reached the expected level. Recently, many researchers have attempted to fill the gap in implementing Arabic chatbots trying to overcome the complex linguistic features of Arabic language. Although Arabic chatbots can be utilized as a supportive tool for Saudi university students, their implementation, especially in Arabic language, is surprisingly neglected. Moreover, during changing the mode of education into distance learning due to (COVID-19) pandemic, it did not help the emergence of Arabic chatbots in Saudi universities. This research focuses on language-related challenges and obstacles that hinder the implementation of Arabic chatbots. Additionally, this paper presents fundamental recommendations for implementing effective Arabic chatbots for academic uses. In summary, it is noticed that Arabic chatbots literature suffers from paucity, which means further studies are needed in this area. [ABSTRACT FROM AUTHOR]

Nowadays, software products have become an essential part of human life. To build software, developers must have a good understanding of the requirements of the software. However, software developers tend to jumpstart system construction without having a clear and detailed understanding of the requirements. The user story concept is one of the practices of the requirements elicitation. This paper aims to present the work conducted to develop an Android chatbot application to support the requirements elicitation activity in software engineering, making the work less time-consuming and structured even for users not accustomed to requirements engineering. The chatbot uses Nazief & Adriani stemming algorithm to pre-process the natural language it receives from the users and artificial mark-up language (AIML) as the knowledge base to process the bot's responses. A preliminary acceptance test based on the technology acceptance model results in an 83.03% score for users' behavioral intention to use. [ABSTRACT FROM AUTHOR]

Adopting technology could give competitive advantage and positively impact the hotel's profitability, thus hotels should keep up with the latest hotel technologies. An important part in the hotel services is the customer service. A problem with the human-to-human customer services today is a long time in answering customers query. On the other hand, nowadays customers need easy and effective services. Thus, a chatbot is required to answer consumers' issues automatically which leads to higher customer satisfaction and a growing profit. Because of the need and there is still an absence of chatbot for hotel industry in Indonesia, this study is conducted. The chatbot for hotel industry in Indonesia, named Bershca, has been successfully developed using artificial intelligence markup language (AIML) to construct the knowledge. Google Flutter is used for the system's front-end, while Python is used for the back-end of the system. As a text-preprocessing method, Nazief-Adriani Algorithm is implemented in the system's back-end. The system is evaluated using technology acceptance model (TAM). As a result, 85.7% of the respondents believe that using chatbot would enhance their job performance and 84.33% of the respondents believe that using the technology would be free of effort. [ABSTRACT FROM AUTHOR]

Chatbot program has been empirically proven help to improve the engagement with users. Moreover, the implementation of a chat-bot program in the industry helps the company to reduce their operational costs in engaging with their customers and employees. There are still quite a number of problems existed in order to build a human-like chatbot program. Understanding a natural conversation and replying back to the conversation the interlocutors, keeping the conversation flowing naturally is a cumbersome task for a computer. This research aims to design an emotionally realistic chatbot system to enhance the believability of the chatbot using Artificial Intelligence Markup Language (AIML) and Information State. The results show that there is a statistically significant improvement to the chatbot believability in the system that has emotions variables induced compare to the one without emotions. Moreover, 63,33% of the respondents perceived Aero and Iris as two different individuals. The future work of this research is to deploy and have an exploration of the chatbot system to other cases. [ABSTRACT FROM AUTHOR]

Agentes conversacionais ou chatbots vêm sendo cada vez mais explorados na educação por trazerem inúmeros benefícios, como permitir que estudantes interajam e se relacionem de forma mais humana com Ambientes Virtuais de Aprendizagem (AVA). Entretanto, desde o surgimento do projeto A.L.I.C.E. (1995) e da linguagem AIML poucas soluções open source emergiram para a criação de chatbots. Apesar de sua facilidade de uso, a AIML tem limitações inerentes a uma lógica baseada em regras. Nesse sentido, este artigo traz uma discussão sobre possibilidades open source existentes para o desenvolvimento chatbots, apresentando sugestões de formas de aprimoramento do uso da AIML com o uso da linguagem de programação JavaScript e o acesso a Bancos de Dados (BD) externos. Resultados do estudo apontam algumas soluções envolvendo as técnicas mencionadas, proporcionando ao chatbot maiores possibilidades de busca e respostas.

Objective: Self-anamnesis is a procedure in which a patient answers questions about the personal medical history without interacting directly with a doctor or medical assistant. If collected digitally, the anamnesis data can be shared among the health care team. In this article, we introduce a concept for digital anamnesis collection and assess the applicability of a conversational user interface (CUI) for realizing a mobile self-anamnesis application.Materials and Methods: We implemented our concept for self-anamnesis for the concrete field of music therapy. We collected requirements with respect to the application from music therapists and by reviewing the literature. A rule-based approach was chosen for realizing the anamnesis conversation between the system and the user. The Artificial Intelligence Markup Language was exploited for encapsulating the questions and responses of the system. For studying the quality of the system and analyzing performance, humanity, effect, and accessibility of the system, we performed a usability test with 22 persons.Results: The current version of the self-anamnesis application is equipped with 63 questions on the music biography of a patient that are asked subsequently to the user by means of a chatbot conversation. The usability study showed that a CUI is a practical way for collecting anamnesis data. Users felt engaged of answering the questions and liked the human characteristics of the chatbot. They suggested to extend the conversation capabilities of the chatbot so that the system can react appropriately, in particular when the user is not feeling well.Conclusions: We could demonstrate the applicability of a CUI for collecting anamnesis data. In contrast to digital anamnesis questionnaires, the application of a CUI provides several benefits: the user can be encouraged to complete all queries and can ask clarifying questions in case something is unclear. [ABSTRACT FROM AUTHOR]

Chatbots are one of the most widely used technologies to implement virtual assistance. Presently, chatbot based virtual assistants are being used by many web administrators to mediate access to data and to carry out generic conversations with the users. Such virtual assistants are getting a lot of attention from the business organizations, as it can help in improving customer care support; reduce the costs in customer service centers and can handle multiple clients at a time. Big data analytics is the process of collecting, organizing and analysing large data sets to discover patterns and unknown correlations hidden in the data, such as usage statistics and customer preferences, which can serve as valuable business information. This paper describes the implementation of a chatbot framework with an interface to big data. This implementation would provide mass knowledge analysis capability to chatbots from distributed environments, which can further the spectrum of usage of such intelligent agents. [ABSTRACT FROM AUTHOR]

Interactivity of corporate website is important as marketing products through it become popular. Moreover, the usage of Artificial Intelligence application is growing in many sectors. In order to improve the interactivity and effectiveness of corporate website in providing information, a conversational bot called SamBot is developed. As a part of Artificial Intelligence application, it can respond to users' question and prolong the conversation with its intelligence. It is integrated to Samsung IoT Showcase website as the corporate website. SamBot contains the knowledge of Samsung marketing domain which is useful to deliver appropriate answer to the users' questions. Its knowledge base includes Samsung promotion, Samsung product Frequently Asked Question, and general knowledge. In default, a conversational bot will generate random answers if matching knowledge cannot be found. In this research, the authors would like to overcome this problem by improving its knowledge maintenance capability either performed by botmaster or users. A function is added to support the knowledge maintenance process by botmaster, namely conversation log-based knowledge maintenance module. As to support consumer-centric knowledge approach, several features are developed such as random answer enhancement and consumer-centric learning capability. This will allow user to directly teach the bots a new knowledge. In addition, enhancement to reuse recently asked question as questions recommendation or autocomplete questions is developed in order to solve a problem where users need to be updated to current issues. With this enhancement, SamBot can give recommendation of questions for users to ask. All of the enhancements are measured using a world-wide Turing Test or Loebner Prize chatbot competition scoring system. The result shows that SamBot is capable of handling Samsung related questions very well with 26.4 points scored. [ABSTRACT FROM AUTHOR]

This paper presents an application to simulate dialogues (chatterbot) using the AIML language (Artificial Intelligence Markup Language) with a new functionality to provide dynamic answers to database queries. This kind of applications is used to answer questions or doubts that are asked by people. Traditional systems are based in fixed answers using AIML language and they search answers in knowledge bases that offer low maintainability, compromising responses that require periodic updates. The AIML language has been extended to be applied to a greater source of knowledge (database).

Chatbot is a piece of software that responds to natural language input and attempts to hold a conversation in a way that imitates a real person. Some chatbots are used for entertainment purposes, while others for business and commercial purposes. Chatbots are getting a lot of attention from business community right now as they can save costs in customer service centers and can handle multiple clients at a time. Successful implementation of a chatbot calls for correct analysis of user's query by the bot and the formation of the correct response that should be given to the user. In many scenarios the information available from the user's query is inadequate to provide the answer. In such contexts, the chatbot needs to be inquisitive so that it will be more interactive and can mimic a more natural human interaction. This paper reports the implementation of an inquisitive chatbot, which finds the missing data in query and probes the questions to users to collect data that are required to answer the query. Through this implementation, the level of interactivity between the user and the chatbot is improved. [ABSTRACT FROM AUTHOR]

There are three defects for providing human-labor customer services in e-commerce operations: high costs of human labors, staff turnover, and lack of service quality assurance. Breakthroughs made in artificial intelligence, natural language processing and related fields make it possible to replace human labors with online artificial intelligent robots to provide the e-commerce customer service, which indicates the online robots are the future of e-commerce customer services. However, most of the current robots are designed to reply with knowledge matching the key words in question sentences from the database, rarely involving in research on customer intentions that are key factors influencing user experience and online sales. In this research, an intention recognizing model was proposed to obtain intentions of e-commerce consumers by computing the strengths of candidate intention nodes in the intention graph, which was used to describe relations between different goods that could be the intentional targets of e-commerce consumers. The proposed robot was constructed based on the intention recognizing model to identify intentions of consumers and use the located knowledge combined with the AIML based sentence composition template to produce the response sentences for consumers. At last, the proposed robot was evaluated using F3 and ROUGE metrics by comparing with a keyword matching robot. And the evaluation results proved the validity of the proposed robot. [ABSTRACT FROM AUTHOR]