Artificial intelligence (AI) has moved past its primitive stages and is now poised to revolutionize various fields, making it a disruptive technology. This technology is expected to completely transform traditional engineering in design, electrical, communication, and renewable energy approaches that have been human-centred. Despite being in its early stages, AI-powered engineering applications can work with vague design parameters and resolve intricate engineering problems that cannot be tackled using traditional design, electrical, communication, and renewable energy methods. This article aims to shed light on the current progress and future research trends in AI applications in engineering concepts, focusing on the ramp-up period of the last 5 years. Various methods such as machine learning, genetic algorithm, and fuzzy logic have been carefully evaluated from an engineering standpoint. AI-powered design studies have been reviewed and categorized for different design stages such as inspiration, idea and concept generation, evaluation, optimization, decision-making, and modeling. The review shows that there has been an increased interest in data-based design methods and explainable artificial intelligence in recent years. The use of AI methods in engineering applications has proven to be efficient, fast, accurate, and comprehensive, particularly with the use of deep learning methods and combinations that address situations where human capacity is inadequate. However, it is crucial to choose the appropriate AI method for an engineering problem to achieve successful results. 
 

Artificial Intelligence in Engineering

Artificial Intelligence (AI) and Machine Learning (ML) have been rapidly advancing in recent years, with many new techniques and models being developed. One area of AI and ML that has more focuses on Pattern Recognition (PR). PR is a subfield of ML that deals with the identification and classification of patterns in data. This field is closely related to other subfields of AI and ML, such as Neural Networks (NNs) and Neuro-Fuzzy Systems (NFS). NNs are a kind of artificial intelligence inspired by the way our brains work. This paper will provide a comparative research of three fields: Neural Networks (NNs), Neuro-Fuzzy Systems (NFS) and Pattern Recognition (PR), highlighting their similarities and differences. NNs, NFS, and PR are three closely related fields of research in the field of AI and ML. The paper begins with a brief introduction to each of these fields, followed by a discussion of their similarities and differences. NNs are a type of AI that are modeled after the function and structure of the human brain system. They integrate a wide-range of interlinked processing nodes, known as neurons that are used to perform various tasks such as PR and control. NNs are particularly useful for tasks that involve large amounts of data, such as image and speech recognition. 

pdf/neural-networks-fuzzy-systems-and-pattern-recognition-a-4q1w2eieja.pdf

Neural Networks, Fuzzy Systems and Pattern Recognition: A Comparative Study

The management of customer services by telephone encounters several problems: an uncontrollable flow of calls, complicated resource management, a very high cost of service, and more. Opportunities to improve the quality of service, save time and money triggered the widespread implementation of artificial intelligence (AI) based callbot. This article outlines the straightforward workflow developed to model the architecture of the callbot. Therefore, several algorithms were evaluated and compared based on real knowledge of a call center of an insurance society. The algorithms considered are: k-nearest neighbours (KNN), support vector machine (SVM), random forests (RF), logistic regression (LR), and Na¨ıve Bayes (NB). The comparison criteria are: correct responses, response time, accuracy, Cohen’s kappa and F1 score using n-gram (1.1) and (2.2). The results obtained show that the SVM (accuracy=70.29%) presents the best results on all the comparison criteria. The comparison between the results of the human agents and the callbot shows an improvement in several levels: the cost savings are greater than 80% on all the tests carried out, the holding time decrease to 0 seconds, and the processing time (almost a third or more). The results obtained sufficiently meet the objectives of this project.

/pdf/modeling-of-an-artificial-intelligence-based-enterprise-11h2glhm.pdf

Modeling of an artificial intelligence based enterprise callbot with natural language processing and machine learning algorithms

Qualitative researchers can benefit from using generative artificial intelligence (GenAI), such as different versions of ChatGPT—GPT-3.5 or GPT-4, Google Bard—now renamed as a Gemini, and Bing Chat—now renamed as a Copilot, in their studies. The scientific community has used artificial intelligence (AI) tools in various ways. However, using GenAI has generated concerns regarding potential research unreliability, bias, and unethical outcomes in GenAI-generated research results. Considering these concerns, the purpose of this commentary is to review the current use of GenAI in qualitative research, including its strengths, limitations, and ethical dilemmas from the perspective of critical appraisal from South Asia, Nepal. I explore the controversy surrounding the proper acknowledgment of GenAI or AI use in qualitative studies and how GenAI can support or challenge qualitative studies. First, I discuss what qualitative researchers need to know about GenAI in their research. Second, I examine how GenAI can be a valuable tool in qualitative research as a co-author, a conversational platform, and a research assistant for enhancing and hindering qualitative studies. Third, I address the ethical issues of using GenAI in qualitative studies. Fourth, I share my perspectives on the future of GenAI in qualitative research. I would like to recognize and record the utilization of GenAI and/or AI alongside my cognitive and evaluative abilities in constructing this critical appraisal. I offer ethical guidance on when and how to appropriately recognize the use of GenAI in qualitative studies. Finally, I offer some remarks on the implications of using GenAI in qualitative studies

pdf/how-can-generative-ai-genai-enhance-or-hinder-qualitative-qdatka8mw2.pdf

How Can Generative AI (GenAI) Enhance or Hinder Qualitative Studies? A Critical Appraisal from South Asia, Nepal

In the contemporary digital era, call centers have significantly incorporated automation through callbots, but they often lack an essential aspect of customer service: empathy. This paper explores the integration of sentiment analysis into call center operations to introduce an emotional dimension to callbot interactions. Utilizing natural language processing and machine learning, the paper examines both text-based and signal-based sentiment analysis approaches. The proposed sentiment analysis architecture encompasses data input and output interfaces, preprocessing, sentiment analysis, and a decision manager module that can, for example, escalate calls to a human agent based on sentiment analysis results. The preprocessing steps, both for text and signal analysis, are outlined in detail, along with a review of relevant sentiment analysis algorithms.Through comprehensive experiments, the study demonstrates that the integration of sentiment analysis yields promising outcomes. In text-based analysis, SVM and LSTM models consistently performed well, achieving accuracy scores of 74% and 72%, respectively. For voice-based analysis, the MLP model exhibited the highest accuracy of 0.72 when using mel spectrogram features, while the RF model outperformed others with an accuracy of 0.78 using MFCC features. These results showcase the potential of sentiment analysis in humanizing callbot interactions, thereby enhancing customer satisfaction and service efficiency.

Call Center Customer Sentiment Analysis Using ML and NLP

Predicting incoming call flows will allow better organization within call centers. This will also allow a reduction in the number of lost calls and lead to the customer’s satisfaction. The purpose of this study is to compare the performance of the machine learning main techniques (random tree forests, k-nearest neighbor, SVM, Naive Bayes) with other Deep Learning algorithms (Perceptron, multi perceptron), and to deduce the best algorithm to apply for a sample of 100,023 calls.

A Comparative Study of Learning Algorithms on a Call Flow Entering of a Call Center

Bots are now more and more present in our daily life, we can find them in the form of chatbots, voicebots or FAQs. Since the number one source of customer demand is phone calls, callbot becomes a requirement in order to ensure a 24/7 presence and to minimize customer wait times in addition to demining charges. Callbots present a new kind of machine-human interface.

Creation of a Callbot Module for Automatic Processing of a Customer Service Calls

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Modeling of an artificial intelligence based enterprise callbot with natural language processing and machine learning algorithms

A Comparative Study of Learning Algorithms on a Call Flow Entering of a Call Center

Call Center Customer Sentiment Analysis Using ML and NLP

Creation of a Callbot Module for Automatic Processing of a Customer Service Calls