Reviewerly/exHarmony huggingface dataset:
Stabilization of a D.C Motor Controller Using an Interval Type-2 Fuzzy Logic System Designed with the Bee Colony Optimization Algorithm

In this paper a Bee Colony Optimization algorithm (BCO) to stabilization of a D.C Motor control is analyzed. The focus of the proposal algorithm is found the distribution of the optimal parameters in the Membership Functions (MFs) of an Interval Type-2 Fuzzy Logic System (IT2FLS). The final experimentation can highlight by implementing the stabilization in an IT2FLS through of the BCO algorithm excellent results are obtained in the evaluation of the Fuzzy Logic Controller (FLC). A comparative analysis with Type-1 FLS and others relevant algorithms is presented to demonstrated and verified that IT2FLS-BCO found minimal errors.
Analyzing and Responding to Google Maps Reviews with a Chatbot in Healthcare

This paper aims to explore how Google Maps reviews for an education and research hospital can be analyzed and responded to using a chatbot. The study highlights the importance of customer feedback in improving hospital services and describes how classification algorithms can be used to collect and analyze reviews. It compares five algorithms to analyze reviews. The chatbot designed in this study responds to reviews and offers personalized suggestions to patients using the most accurate one among five algorithms. The findings suggest that automated chatbot responses can save time and resources while improving the hospital’s online reputation. The study concludes that implementing a chatbot for Google Maps reviews can enhance patient satisfaction and lead to better overall service quality. Among the five classification algorithms used within the scope of the study, it was determined that Naive Bayes and Neural Networks algorithms gave the highest accuracy rate with 79% when categorizing the comments according to the subject and performing the sentiment analysis at the same time. However, other algorithms’ success rates are similar, and the chatbot responds to people by using the results of the algorithm with the highest success rate for each newly entered sentence.
Assessment of Cancer Detection from CT Scan Images Using Hybrid Supervised Learning Methods

Cancer is one of the deadliest and most ubiquitous diseases, and it claims a lot of lives every year. Radio diagnosis has a significant impact on medical image processing. Among all the main cancers, lung and liver cancers are the most prevalent and has the highest mortality rate these days. The current condition makes it difficult and time-consuming to manually identify the cancer tissue. Assessing the tumor load, planning therapies, making predictions, and tracking the clinical response can all be done using the segmentation of lesions in CT scans. In order to address the current problem of liver cancer, a Hybridized Fully Convolutional Neural Network, which is potent method for analyzing liver cancer in terms of semantic segmentation, has been proposed in this research for liver tumor segmentation. Dilation and erosion are two processes that are combined in this procedure. The goal of the proposed technique is to draw attention to the tumor region shown on the CT scan. And as part of the algorithm for the identification of lung cancer, the segmentation of the lung region of interest using mathematical morphological procedures is recommended. For picture pre-processing, this algorithm also employs techniques like median filtering. Support vector machines are used to classify CT scan pictures and compute geometric attributes from the recovered region of interest. Here, Computed Tomography (CT) pictures are used to identify malignant liver tumors. The image is improved using anisotropic diffusion filters and segmented using straightforward morphological techniques.
An Application of Fuzzy ANOVA on Field of Agricultural Machinery

ANOVA is a statistical analysis test that addresses the significance of variances in the means of different populations. If the data includes imprecise or vagueness in the applied sciences, the classical ANOVA cannot be the solution. In which cases, fuzzy ANOVA based on fuzzy observations ought to be used and are particularly more suitable for such problems in contrast to ANOVA, Fuzzy ANOVA (FANOVA) uses fuzzy numbers and is a generalization of ANOVA. In recent years, however; particularly, fuzzy statistical methods have been put into use for the designing process of agricultural machinery. ANOVA for the studies that have been put out regarding literature, there is not a study present that uses the FANOVA method. Therefore, in this study, span, which is one of the foremost factors to be taken into consideration for the design of an agricultural machine to be developed for harvesting grainy products, on the max force that can be applied to a chestnut was evaluated and the results were assessed.
An Intelligent Data Analysis Approach for Women with Menopausal Genitourinary Syndrome with Intuitionistic Fuzzy Logic

Genitourinary syndrome of menopause is a set of symptoms that develop after and around menopause, due to low estrogen levels and hypo- and atrophic changes in the genitourinary tract of women. The study analyzed data on 67 patients with genitourinary syndrome of menopause after surgical and medical (hormonal and chemotherapy) castration aged 30 to 45 years. 35 patients had a surgical castration and 32 patients are with drug therapy (chemo and/or hormone therapy). For the analysis an InterCriteria Analysis method is used. It is based on two mathematical theories: the theory of intuitionistic fuzzy sets, and the theory of index matrices. The investigated data contains information about different parameters: Age, Type of castration, Main disease, Therapy carried out, Vaginal PH, Vaginal cleanliness, Amount of lactobacilli, Subjective symptoms related to genitourinary syndrome, Vulvovaginal symptoms, etc. By applying the ICA method, the dependencies between the criteria (clinical results) by which the different patients are evaluated were investigated. The correlations between each pair of criteria are determined in the form of intuitionistic fuzzy pairs of values in the interval [0; 1]. This supports the decision-making process in patients with oncological disease (cervical cancer, mammary carcinoma, ovarian carcinoma) after surgical and medical castration regarding vaginal cleanliness, lactobacilli count, vaginal pH and subjective symptoms of vaginal and urinary component of menopausal genitourinary syndrome. As a result of the study, conclusions were made about the predictors of the genitourinary syndrome.
An Overview of Few-Shot Learning Methods in Analysis of Histopathological Images

Analysis of histopathological images allows doctors to diagnose diseases like cancer, which is the cause of nearly one in six deaths worldwide. Classification of such images is one of the most critical topics in biomedical computing. Deep learning models obtain high prediction quality but require a lot of annotated data for training. The data must be labeled by domain experts, which is time-consuming and expensive. Few-shot methods allow for data classification using only a few training samples; therefore, they are an increasingly popular alternative to collecting a large dataset and supervised learning. This chapter presents a survey on different few-shot learning techniques of histopathological image classification with various types of cancer. The methods discussed are based on contrastive learning, meta-learning, and data augmentation. We collect and overview publicly available datasets with histopathological images. We also show some future research directions in few-shot learning in the histopathology domain.
Medical Microscopic Single Image Super-Resolution Based on Deep Neural Networks

Hematological diseases affecting the blood and hematopoietic organs are detected using digital microscopy systems in clinical laboratories. However, the quality of such microscopic images often remains poor due to expensive coloring components. There are many methods, including traditional image processing and segmentation methods, which are currently being implemented using deep neural networks. One of the promising ways is to apply super-resolution approach, which has also been developed from interpolation methods to super-resolution (SR) methods based on deep neural networks. In this chapter, we study two fundamentally different approaches for microscopic image reconstruction: the non-GAN-based SR model and the GAN-based SR model. The GAN-based SR model was significantly modified during experimental research. Due to the need to preserve contours and textures in a high-quality representation, the outputs were fed and processed by deep wavelet SR network. We provide rich experimental results for microscopic image reconstruction on two public datasets “Blood cell images” and “Malaria bounding boxes” from Kaggle cloud storage using different deep SR models.
Contextual Bipolar Database Queries: A Conjunctive and Disjunctive Perspective

We extend our approach to bipolar database queries which makes it possible to use a necessary (required, mndatory, obligatory) and optional (desired) condition connected with a non-conventional aggregation operator “and, if possible” exemplified by “find a house which is “inexpensive” and, if possible, located possibly ‘close to public transportation”’ in which “and, if possible,...” is not the traditional “and”. We extend first this formulation by context exemplified by “find houses which are inexpensive and – if possible, with respect to other houses in town – are possibly close to public transportation”. We use a logical representation of the “and, if possible...” operator but also mention some other ones, notably based on the winnow operator. Moreover, we present a new formulation of a contextual bipolar query, “find a house which is inexpensive or - if impossible, with respect to other houses in own – is new”. We mention an inclusive and exclusive character of these two formulations. We present a context awareness related view of these bipolar queries, and provide some remarks on intention awareness.
Blockchain: Architecture, Security and Consensus Algorithms

Blockchain is an innovative technology that is making a high impression on current society through its precision, decentralization, and high-security assets. In this research, we provide a survey of the different architecture, security, and consensus algorithms of Blockchain technology. We also provide a comparison between different Blockchain frameworks and classification of consensus algorithms that are mostly used as well as analyzing security risks and cryptographic primitives currently being used in the Blockchain. This paper also elaborates on important future directions, creative application cases, and unresolved research difficulties that could be investigated by scientists to enhance this subject.
Intuitionistic Fuzzy Modal Topological Structures Based on Two New Intuitionistic Fuzzy Modal Operators

In the present paper, four Intuitionistic Fuzzy Modal Topological Structures (IFMTSs) are described and some of their properties are discussed. While in the first paper over an IFMTS the modal operators were the simplest (standard) ones, here particular cases of three of the extended modal operators over intuitionistic fuzzy sets are used. Some properties of the new operators are discussed.
Aggregation Operators for Face Recognition

In this paper we consider the aggregation functions’ application to problems of face recognition. A wide range of aggregation function classes can play a crucial role in multi-criteria decision-making theory. Some of the most well-known aggregation functions include generalized means, integrals based on non-additive measures, etc. Their usage also saves memory resources and can lead to an increase in the percentage of correctly classified objects (in our case, we deal with object recognition). Biometry and face recognition represent important modern problems in computer science, and some solutions are widely-adopted and applied in mobile devices and identity confirmation, border control, ID and other document verification, etc. Other than classic methods for face recognition, such as PCA, linear discriminant analysis, as well as various local descriptors and other methods, methods based on sparse data representation and deep learning methods have recently been used. Each part of the face can be compared individually, and a conclusion regarding the match-overlap of two faces should be drawn from all these comparisons. In our investigation, we analyze the quality of the decisions made in face recognition problems, with regards to the aggregation functions used, as well as the different methods used for recognizing certain parts of the face.
Kevin Kwan’s Crazy Rich Asians: Opinion Mining and Emotion Detection on Fans’ Comments on Social Media

The Singapore-born American novelist Kevin Kwan’s Crazy Rich Asians (2013) and its cinematic adaptation have evoked conflicting responses from the public in social media. Analyzing the huge amount of data posted online is an arduous task for literary scholars who use traditional methods to understand readers and viewers’ responses to creative works of art. This study offers a data science approach for opinion mining and detecting embedded emotions in people’s comments. To achieve this goal, a Machine Learning Model Classifier is developed to analyze embedded emotions in public responses to the movie adaptation of Kevin Kwan’s Crazy Rich Asians on YouTube. A total of 41,161 comments posted before or on 27 December 2021 are collected using the YouTube Data API. An emotion classifier with Incremental Learning Approach is built to detect emotions associated with the comments. The built emotion classifier showed accuracy ranging from 84% to 93%. The findings reveal that most of the comments are associated with the emotions of curiosity, love, and surprise.
Intelligent Approaches

The term “intelligent approaches” in the context of soft computing generally refers to methods that involve the use of artificial intelligence (AI) techniques to solve problems.
Circular Intuitionistic Fuzzy Analysis of Variance on the Factor Season of Apple Sales

The circular intuitionistic fuzzy sets developed by Atanassov are a more versatile tool for defining economic fuzziness than the intuitionistic fuzzy ones. ANOVA is a technique for determining how a certain factor affects the event or process under research. This study extends the intuitionistic fuzzy analysis of variance (IFANOVA) to a circular IFANOVA (C-IFANOVA), where all of its parameters are circular intuitionistic fuzzy numbers. Pessimistic, optimistic, and averaging possibilities are presented to the decision-maker for a final decision. To check the created approach of C-IFANOVA and demonstrate its efficacy, a comparison analysis is conducted using a traditional variation analysis on the sales of the Apple firm for 2019-2022, broken down by the factor “season”.
Recommending Open Educational Resources Using Weighted Linear Combination

Open Educational Resources are learning, instructional, and research materials in any type. However, diverse groups of learners can gain access in open educational resources. As a result, many users may have difficulties in finding precise material that matches their preferences. In response to this pressing demand, this paper presents a recommender system for suggesting open educational resources. To refine the delivered material to learners, the recommender system employs the weighted linear combination model. It also takes into account many learner and material characteristics. The recommender system generates material suggestions for learners based on their liking and interests. The proposed recommender system was evaluated with promising results.
Motivations, Development Challenges and Project Desertion in Public Blockchain: A Pilot Study

Blockchain technology has attracted tremendous interest from the software development community. For instance, GitHub currently hosts many active blockchain projects. As a result, information systems research has investigated these projects and their developer community. Although the number of these projects is growing rapidly, the motivations and development challenges of developers remain a mystery. Also, there is a perception that the scarcity of development tools, which is obvious for new technology, can influence project turnover. Therefore, the primary objective of this study was to evaluate the reliability and validity of an instrument developed from the identified motivations and development challenge factors that might lead to project desertion derived from open-source software (OSS) literature and social cognitive theory. This pilot study examined a small sample of data in order to assess the study’s construct validity and dependability. The survey method was used to collect the data. The results of 66 actual surveys from blockchain developers were compiled using the stratified random sample method and the data were analyzed using IBM SPSS version 28.0.0.0 for Windows. According to the results, the adapted instruments used in the pilot study are reliable and valid.
Intelligent Chatbots for Electronic Commerce: A Customer Perspective

Customer service plays a vital role in electronic commerce (EC) applications. However, the cost of providing customer service to meet customers’ needs is high. The use of chatbots has helped to reduce this cost. A chatbot is an artificial intelligence application that imitates and processes human dialogue. Chatbot applications still have deficiencies in some aspects. Thus, we conducted this research to measure chatbot users’ perspectives of EC websites and applications. We used a survey to elicit opinions about several aspects of EC chatbots: (i) their importance, (ii) performance, (iii) usage preference, and (iv) the area where they are most needed. Furthermore, we have introduced several positive and negative opinions as well as some suggestions from the customer’s perspective. Our aim in this research is to assist stakeholders, including EC website and application owners and chatbot service developers, in improving the provided services and solving identified problems. The main output of this research is the analysis of users’ expectations when dealing with chatbots, in addition to the strengths and weaknesses of chatbot performance, which can be used for chatbots improvement. The results showed that 69% of participants believed that fast response is the main advantage of chatbots. In contrast, 31% believed that the biggest problem is the lack of chatbots understanding of some customers’ questions.
Augmented Reality as a Learning Media to Improve Vocabulary Learning Among Preschoolers

Technology is crucial to education since it has spurred numerous innovations and advancements in the educational system. At the moment, augmented reality (AR) is thought to be one of the technologies that could have a significant impact on the field of education. The visual and interactive aspects of augmented reality, which enable the projection of digital content into the user’s field of view, have enormous promise as educational tools. For students, especially young ones, it might be helpful to design simulations that keep them attentive and interested in what they are studying. Therefore, the goal of this study is to enhance pre-schoolers’ vocabulary learning through the use of augmented reality. To accomplish the goal of this study, an AR application called the Home AR is created. The application’s focus is on teaching the children between the ages of 4 and 6; the names of everyday objects are in four distinct languages: English, Malay, Tamil, and Chinese. The furniture and appliances that are found in the living room, bathroom, bedroom, and kitchen are chosen to be displayed in this application. To display the images of home goods that can be scanned from the AR application, flashcards are created as markers. The performance of the students utilising the developed application and the conventional learning approach is compared in an experimental study. Additionally, a variety of evaluation techniques are used to examine how parents, teachers, and pre-schoolers feel about adopting augmented reality (AR) as a learning tool for vocabulary development. The findings gathered for this study demonstrated that pre-schoolers can efficiently acquire languages through AR. The results show that AR has a high potential for use as a revenue-generating medium in the future and can be used for learning.
FM_STATE: Model-Based Tool for Traceability, Generation, and Prioritization in Software Product Line Engineering

Software Product Line (SPL) testing requires constant improvement due to current demands. This technique is a challenging task requiring needs of multiple quality methods. Model-Based Testing (MBT) is a method that can reduce testing effort. However, the existing approach emphases on single-phase testing, resulting to only minor improvement in testing result. To handle this issue, this study introduces FM_STATE, a tool that comprised three phases of testing which are mapping features through behavior elements, generating test cases, and prioritizing test cases. This method is analyzed by using five evaluation methods based on maximization and minimization. The exclusivity of FM_STATE is accepts the feature and behavior elements from.xml files and allows the management of all kinds of SPL model sizes from small to large.
A Deep Learning Model for Human Blood Cells Classification

Microscopic imaging is gaining focus in recent days, especially in the part of histopathological image analysis. Blood cells plays a critical role in assessment of health status of patients, especially given the rising frequency of infectious diseases. Automated blood analysis can aid in detecting early stages of diseases. In this work, we present study classification of Blood cell using different transfer learning approaches, MobileNetV2 based model designed for the accurate multi classification of blood cells. Deep Learning (DL) models require more time when training on big data sets, to overcome the computation complexity with light weight model MobileNetV2 is considered. In this paper we present the comparison among different transfer learning models such as VGG16, VGG19, Resnet50 with MobileNetV2. The performance evaluated with Accuracy, Precision, recall and F-Score. MobileNetV2 outperform all other model with accuracy of 97.89%. The proposed model has improved accuracy for classification blood cell for eight class.
Cybersecurity Assessment Construction of Artificial Intelligence

The development of artificial intelligence (AI) in the automotive sector is a major contributor to the increasing number of intelligent transportation systems. Especially, with the rapid emergence and evolution of machine learning and artificial intelligence (AI) capabilities the number of Al models used in the automotive industry has been developed. Within this context; as an automotive company; we have provided efficient and secure AI models that can be used in the autonomous driving industry, and we have regularly investigated and provided the necessary security attributes to ensure that the Al models used in the autonomous driving industry are equipped with the necessary security attributes. From this perspective, the aim of this paper is to provide an overview of the various aspects of the intellectual property eco-system, including the current status of standardization and academic perspectives, also highlights the potential of this area. For this purpose we have performed a D-patching attack on the Yolo v3 model (Mujahid, 2021) to compromise its integrity. The results show that these methods are effective in protecting the model, but they require additional costs to be considered. Finally, To prevent this attack, we proposed a set of countermeasures that can be used to detect the input testing dataset.