Reviewerly/exHarmony huggingface dataset:
Noninvasive Detection of Alzheimer’s Disease from Conversational Speech Using 1D-CNN

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects the normal functioning of a person. Typical Alzheimer’s symptoms include loss of cognition, loss of speech, and forgetting events. The progression is irreversible. Early detection of AD is essential to delay the progressive nature of the disease. Current invasive and non-invasive diagnostic methods cost time and money. We propose an inexpensive approach to detect AD from speech conversations. Acoustic features such as pitch, jitter, shimmer, zero-crossing rate, and voice activity were obtained from speech conversion. The dimensionalities of the acoustic features were reduced with principal component analysis (PCA). Original acoustic features and PCA-derived were fed to one-dimensional convolutional neural networks (1D-CNNs) and support vector machine (SVM) model to classify the subject as AD or not. Empirical findings report that the 1D-CNN gave an accuracy of 90% better than SVM. We are optimistic that our models would benefit the medical research community as an alternative method in diagnosing AD at an early stage.
A Generalization of Fuzzy c-Means with Variables Controlling Cluster Size

This study constructs two general fuzzy clustering algorithms with a cluster size controller. The first algorithm includes the standard fuzzy c-means (SFCM), modified SFCM, and generalized fuzzy c-means, and the second one includes the entropy-regularized fuzzy c-means (EFCM), modified EFCM (mEFCM), and regularized fuzzy c-means (RFCM). Furthermore, the results of this study demonstrate that the behavior of the fuzzy classification functions of the first proposed algorithm at points far from clusters are similar to that for mSFCM, and those of the second one are similar to those for EFCM, mEFCM, and RFCM. some conventional clustering algorithms.
An Evaluation of Feature Selection Methods Performance for Dataset Construction

A significant growth of digital data in different applications increases the data size and storage. Digital data may include missing values, irrelevant information, incorrect values, and redundant features. Each attribute in data collection is called features of dimensions. More dimensions in a dataset make prediction a complicated task. Feature selection is a method that plays a vital role in reducing the dimension of data and it can be done as an initial step in processing. Feature algorithm extract the refined feature for better classification and accuracy of predictive models. The proper selection of features is used to increase the efficiency of a dataset and performance of a model. Feature selection methods are not only used to reduce dataset but also to reduce the overfitting problems in mining process. This paper presents various feature selection methods in order to extract consistent data. The algorithms such as CFS, CAE, IGE, GRE, and WSE are used to select features. To measure the performance of these selected feature Naive Bayes (NB) model and support vector machine (SVM) model are used. Experimental result shows CAE, GRE, and IGE with SVM model give better performance than other methods.
Classification of Breast Invasive Ductal Carcinomas Using Histopathological Images Based on Deep Learning Techniques

Women suffer from cancer, which is the main reason for death for females around the world. With the use of artificial intelligence, it is possible to predict and detect all types of cancers in the near future. It is not just women who can heal, and most breast cancers are caused by the most vulnerable type of breast. Eighty percent of all diagnoses of carcinoma are invasive ductal carcinomas (IDCs). In this paper, deep learning techniques are extended to support visible semantic evaluation of tumor areas, using convolutional neural networks (CNNs). A CNN is skilled ended a large number of photo covers (tissue areas) after Whole Slide Images (WSI) to study ranked part-based total image. About 600 normal image patches and 200 breast invasive ductal carcinomas are selected for the experiment. It was intended to amount classifier correctness in the detection of IDC tissue areas in Whole Slide Images. We achieved excellent measurable outcomes for an automated finding of IDC areas with our technique. The results are evaluated based on performance measures and compared with a different number of neurons, and the results are highlighted.
Extremist Views Detection: Definition, Annotated Corpus, and Baseline Results

Extremist view detection in social networks is an emerging area of research. Several attempts have been made to extremist views detection on social media. However, there is a scarcity of publicly available annotated corpora that can be used for learning and prediction. Also, there is no consensus on what should be recognized as an extremist view. In the absence of such a description, the accurate annotation of extremist views becomes a formidable task. To that end, this study has made three key contributions. Firstly, we have developed a clear understanding of extremist views by synthesizing their definitions and descriptions in the academic literature, as well as in practice. Secondly, a benchmark extremist view detection corpus (XtremeView-22) is developed. Finally, baseline experiments are performed using six machine learning techniques to evaluate their effectiveness for extremist view detection. The results show that bigrams are the most effective feature and Naive Bayes is the most effective technique to identify extremist views in social media text.
Adapting the Gini’s Index for Solving Predictive Tasks

The most used technique for solving prediction tasks is regression analysis which approximates the data at hand using a polynomial function. To construct a regression model is necessary either to have a deep knowledge of the domain or to use some attribute selection method to select the appropriate variables that will form the model. In the present paper, we introduce P-LID, a new predictive method based on ideas coming from both lazy and inductive learning methods. P-LID takes two ideas from inductive models: one is to select the relevant attributes to classify new objects and the other is to justify the classification. A technique for selecting relevant attributes is to use Gini’s index, but this is only applicable when attributes have continuous values and the class is categoric. In P-LID we propose an adaptation of the Gini’s index to use it when the values of the classes are continuous.
REST, GraphQL, and GraphQL Wrapper APIs Evaluation. A Computational Laboratory Experiment

This research studies the effects of development architectures on the quality of APIs by conducting a computational laboratory experiment comparing the performance efficiency of a GraphQL API, a REST API, and a GraphQL API that wraps a REST API. Open data from the Electronic Chamber of Commerce of Ecuador, part of a national e-commerce research project, was used. To characterize quality, we used ISO/IEC 25,023 metrics in different use cases of e-commerce data consumption and insertion. Finally, we statistically analyzed the experiment results, which indicate a difference in quality between the REST API, the GraphQL API, and the GraphQL API (wrapper); this being the case, the GraphQL API performs more efficiently.
Covid-19 Question-Answering System Based on Semantic Similarity

Virtual assistants are now a part of our daily lives and have replaced humans in most web-based query systems. The challenge in designing such conversational agents is to replace scripted responses with answers that mimic the way a human communicates with another human. In this work, the task is to create an automated virtual Covid-19 QA agent that would answer user queries related to Covid-19. Since the outset of the Covid-19 pandemic and resulting lockdowns, people have started relying on virtual healthcare agents to resolve their health issues. Many a times, questions worded in different ways have the same meaning and a similar response could be generated for all such cases. Computation of a semantic similarity score between sentence pair representations would facilitate improved natural language understanding. The sentence representations are derived from the hidden-state representations of different neural networks. The proposal in this paper is to investigate state-of-the-art LSTM-based frameworks for application to Covid-19 QA systems that involve computation of semantic similarity between sentence pairs. Experiments performed on the benchmark Covid-19 QA dataset containing question–answer pairs related to the Covid-19 pandemic prove that the Siamese LSTM model generates the best responses than the baseline models.
Disease Diagnostic Model Using Fine-Tuned Intensive Learning for Medical Image

Computer-Aided Diagnostic (CAD) techniques use image-based categorization as an effective method. Existing approaches rely primarily on structure, coloring, texture-based, and mixtures. The majority of these is issue-specific and demonstrate complementary in medical images, resulting in a scheme that cannot represent high-level problem functional requirements and has impoverished prototype generalization capability. Modern deep neural networks have made it possible to build an edge model which can generate the best classification categories from primary healthcare image data. The Disease Diagnostic Model utilizing Fine-Tuned Intensive Learning (DDM-FTIL) classification offers superior characteristics generated from a convolution neural network (CNN) with some classical characteristics. Adaptive mean filtering and adaptive histogram are used to strengthen and optimize the pixel density. We use the Gabor filter to convert the improved images. The characteristics are retrieved by the Gray-Level Co-occurrence Matrix (GLCM) as well as Local Binary Pattern (LBP) descriptors before classing the DDM-FTIL classifier. Oral cancer areas were segmented using morphometric approaches.
Conformal Prediction for Accuracy Guarantees in Classification with Reject Option

A standard classifier is forced to predict the label of every test instance, even when confidence in the predictions is very low. In many scenarios, it would, however, be better to avoid making these predictions, maybe leaving them to a human expert. A classifier with that alternative is referred to as a classifier with reject option. In this paper, we propose an algorithm that, for a particular data set, automatically suggests a number of accuracy levels, which it will be able to meet perfectly, using a classifier with reject option. Since the basis of the suggested algorithm is conformal prediction, it comes with strong validity guarantees. The experimentation, using 25 publicly available two-class data sets, confirms that the algorithm obtains empirical accuracies very close to the requested levels. In addition, in an outright comparison with probabilistic predictors, including models calibrated with Platt scaling, the suggested algorithm clearly outperforms the alternatives.
Comparative Analysis of Machine Learning Algorithms for Author Age and Gender Identification

Author profiling is part of information retrieval in which different perspectives of the author are observed by considering various characteristics like native language, gender, and age. Different techniques are used to extract the required information using text analysis, like author identification on social media and for Short Text Message Service. Author profiling helps in security and blogs for identification purposes while capturing authors’ writing behaviors through messages, posts, comments, blogs, comments, and chat logs. Most of the work in this area has been done in English and other native languages. On the other hand, Roman Urdu is also getting attention for the author profiling task, but it needs to convert Roman-Urdu to English to extract important features like Named Entity Recognition (NER) and other linguistic features. The conversion may lose important information while having limitations in converting one language to another language. This research explores machine learning techniques that can be used for all languages to overcome the conversion limitation. The Vector Space Model (VSM) and Query Likelihood (Q.L.) are used to identify the author’s age and gender. Experimental results revealed that Q.L. produces better results in terms of accuracy.
Disability Assistance System Using Speech and Facial Gestures

Physically disabled people are deprived from their opportunities in computer technologies stating their disabilities. Therefore, we are in need of developing a system which can help the disabled to interact with the computer technologies overcoming their disability. The main objective of this paper is to propose an assistance system for the disabled using minimum amount of resources and work for users with specialized interfaces. This proposed system can be helpful for inclusion of persons with amputee and vision impairment to use the latest technologies with easy accessibility. The user can interact with the system using two options called head-based control or voice-based control. This system can create many job opportunities in today’s computer world which can uplift the social and economic status of the disabled person.
Bipolar OWA Operators with Continuous Input Function

Mesiar et al. in 2020 introduced discrete bipolar OWA operators and in 2022 a further investigation on discrete bipolar OWA operators was published by the same authors. They introduced also the abbreviation BIOWA. In the paper, BIOWA operators with continuous input functions are proposed and studied. Also the orness measure of continuous BIOWA is introduced.
Formal Languages and the NLP Black Box

The field of natural language processing (NLP) has been transformed in two related ways in recent years.
Voice Operated Fall Detection System Through Novel Acoustic Std-LTP Features and Support Vector Machine

The ever-growing old age population in the last two decades has introduced new challenges for elderly people such as accidental falls. An accidental fall in elderly persons results in lifelong injury, which has extremely severe consequences for the remaining life. Furthermore, continued delay in the treatment of elderly persons after accidental fall increases the chances of death. Therefore, early detection of fall incidents is crucial to provide first aid and avoid the expenses of hospitalization. The major aim of this research work is to provide a better solution for the detection of accidental fall incidents. Most automatic fall event detection systems are designed for specific devices that decrease the flexibility of the systems. In this paper, we propose an automated framework that detects in-door fall events of elderly people in the real-time environment using a novel standard deviation local ternary pattern (Std-LTP). The proposed Std-LTP features are able to capture the most discriminatory characteristics from the sounds of fall events. For classification purposes, we employed the support vector machine (SVM) to distinguish the in-door fall occurrences from the non-fall occurrences. Moreover, we have developed our fall detection dataset that is diverse in terms of speakers, gender, environments, sample length, etc. Our method achieved an accuracy of 93%, precision of 95.74%, recall of 90%, and F1-score of 91.78%. The experimental results demonstrate that the proposed system successfully identified both the fall and non-fall events in various indoor environments. Subsequently, the proposed system can be implemented on various devices that can efficiently be used to monitor a large group of people. Moreover, the proposed system can be deployed in daycare centers, old homes, and for patients in hospitals to get immediate assistance after a fall incident occurs.
The Framework for Mobile Robot Task Planning Based on the Optimal Manufacturing Schedule

Integrated process planning and scheduling of manufacturing entities represents one of the most impactful processes for optimizing production systems. The result of integrated process planning and scheduling is visually presented in the form of the Gantt chart that summarizes the timing and order of all manufacturing operations. Moreover, a sequence of actions that needs to be performed by a transportation system to ensure the smooth execution of all manufacturing processes is also incorporated within the Gantt chart. Therefore, in this paper, we develop a framework for mobile robot action planning based on high-level action, e.g., transporting the part from one machine to another. The idea is to automatically plan a sequence of robot-performable actions that, if performed accurately, results in achieving a high-level goal. The framework is developed in the standard Robot Operative System 2 (ROS2) middleware. The system, domain, and essential entities are modeled by using Planning Domain Definition Language (PDDL). The mobile robot actions are modeled by Behavior trees within the PlanSys2 framework. Even though the system for task planning is developed for a manufacturing environment, the framework can be used in various fields such as service or medical mobile robotics applications where scheduling activity is of the essence.
Approaching Repetition Thresholds via Local Resampling and Entropy Compression

We analyze a simple algorithm, transforming an input word into a word avoiding certain repetitions such as fractional powers and undirected powers. This transformation can be made reversible by adding the log of the run of the algorithm to the output. We introduce a compression scheme for the logs; its analysis proves that $$(1+\frac{1}{d})$$ -powers and undirected $$(1+\frac{1}{d})$$ -powers can be avoided over $$d+O(1)$$ letters. These results are closer to the optimum than it is usually expected from a purely information-theoretic considerations. In the second part, we present experimental results obtained by the mentioned algorithm in the extreme case of $$(d+1)$$ -ary words avoiding $$(1+\frac{1}{d})^+\!$$ -powers.
Lane Sensing and Tracing Algorithms for Advanced Driver Assistance Systems with Object Detection and Traffic Sign Recognition

Advanced driver assistance systems (ADASs) and autonomous vehicles are expected to increase safety, lower energy and fuel consumption, and lower pollutants from road traffic. The advanced driver assistance system’s major features include lane detection and tracking. Finding color line marks on the road is the technique of lane detection. The process of lane tracking aims to help the vehicle continue traveling along a predetermined course. Hence, automatic detection of lanes using convolutional neural networks (CNNs) models has gained popularity in the current economic development. This paper also aims at detecting various objects using convolutional neural networks. In this work, we offer object classification and detection, a demanding topic in computer vision and image processing. As a result, we deployed convolutional neural networks on the Keras platform with TensorFlow support. The experimental results illustrate the amount of time needed to train, test, and generate the model using a constrained computing environment. Here, we trained the system for about 80 images which have taken a couple of seconds to detect with better accuracy. Traffic sign recognition is carried out, which is a significant area of research in ADAS. It is crucial for driverless vehicles and is frequently used to read stationary or moving road signs along the side of the road. A comprehensive recognition system is made up of traffic sign detection (TSD) and categorization (TSC). The paper also aimed at traffic sign recognition, which is crucial to consider, because traffic sign recognition is typically applied to portable devices. The model’s detection accuracy is ensured as long as the speed is maintained. The model developed in this study is 99.89% accurate.
Abstractive Text Summarization of Biomedical Documents

Huge amounts of data has to be processed and toned down to coherent simpler forms in order to be made sense out of. Abstractive summarizers generate summaries similar to how humans would summarize a document, consisting of new words and phrases in a simple, readable format. With vast quantities of information getting generated in the medical domain every day, there’s a huge demand to summarize complex medical information for the benefit of both doctors and patients alike. This includes pre-processing biomedical documents and building an abstractive text summarizer using LSTM Encoder-Decoder model with attention layer. The LSTM encoder reads the entire input sequence, with one word fed into the encoder at each timestep. The decoder is likewise an LSTM network that predicts the same sequence with a one-timestep offset after reading the complete target sequence word-by-word. Using fresh source sequences for which the target sequence is unknown, the summary generated thereby can be evaluated for its accuracy. Extractive summarization of medical documents has been explored but there hasn’t been much work done on abstractive summarization for the same. We aim to provide concise summaries of medical documents with a nearly 2% higher accuracy as compared to the existing methods.
Modeling Volatility of Cryptocurrencies: GARCH Approach

Since its inception in 2009, cryptocurrency’s acceptance remains controversial because of its nature, absence of inherent worth, and ambiguous issuance authority. This resulted in the significant volatility and unpredictability of the cryptocurrency price, leading to the loss of investors’ funds. In this study, we model the heteroskedastic volatility of cryptocurrencies and forecast the future price using GARCH. Historical analysis of cryptocurrencies shows volatility ranging from medium to extremely high volatility. Like other financial assets, cryptos exhibit volatility clustering, i.e., high volatility leading to further high volatility and low volatility leading to low volatility forming volatility clusters. In this paper, we apply seven variants, namely Standard GARCH (SGARCH), IGARCH (1,1), EGARCH (1,1), GJR-GARCH (1,1), Asymmetric Power ARCH (APARCH) (1,1), Threshold GARCH (TGARCH) (1,1), and Component GARCH (CGARCH) (1,1), of the GARCH model to forecast the volatility of cryptocurrencies and identify the best-fit model based on forecasting accuracy. We find that among the seven variants of GARCH, EGARCH and CGARCH models appropriately forecast the volatility of cryptocurrencies. We conclude that exponential and component GARCH provides the appropriate future price of meme and utility coins in the short run.