A novel CNN architecture for accurate early detection and classification of Alzheimer's disease using MRI data

Alzheimer's disease (AD) is a debilitating neurodegenerative disorder that requires accurate diagnosis for effective management and treatment. In this article, we propose an architecture for a convolutional neural network (CNN) that utilizes magnetic resonance imaging (MRI) data from the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset to categorize AD. The network employs two separate CNN models, each with distinct filter sizes and pooling layers, which are concatenated in a classification layer. The multi-class problem is addressed across three, four, and five categories. The proposed CNN architecture achieves exceptional accuracies of 99.43%, 99.57%, and 99.13%, respectively. These high accuracies demonstrate the efficacy of the network in capturing and discerning relevant features from MRI images, enabling precise classification of AD subtypes and stages. The network architecture leverages the hierarchical nature of convolutional layers, pooling layers, and fully connected layers to extract both local and global patterns from the data, facilitating accurate discrimination between different AD categories. Accurate classification of AD carries significant clinical implications, including early detection, personalized treatment planning, disease monitoring, and prognostic assessment. The reported accuracy underscores the potential of the proposed CNN architecture to assist medical professionals and researchers in making precise and informed judgments regarding AD patients.

A novel intelligent agent-based framework for appropriate stream selection from perceptive of career counseling

Picking a career stream profoundly influences people's abilities in different ways. Nowadays, choosing the correct career stream in advanced education is troublesome, as the number of people wanting to be in specific specializations is growing. Therefore, it is essential to be able to select the appropriate career path. This article proposes a system that can suggest streams in advanced education schools. This system is influenced by agent-based stream proposal systems (ASPS). The proposed system aims to make picking out the correct stream to study at school simpler for an individual. Different streams are evaluated based on seven pre-characterized models. In our system, three unique sorts of tests, learning styles, and coaching were coordinated in a way that caused the system to recognize an individual's interests and limits to an area of study. A sample of 238 participants was recruited for our questionnaire on accessibility, user-friendliness, accuracy, and satisfaction with the system. The incorporation of learning styles and coaching proved helpful in the study. The reliability and validity were proven in addition to incorporating a thinking-aloud protocol and immediate evaluation in the pre-, during and post-tests. To a large extent, respondents were satisfied with the model, as was revealed in the Likert scale response frequencies and percentages. The proposed system can be applied to undergraduates choosing engineering, medicine, arts and science degrees.

Air Pollution and Health Risk: Intelligent Mapping

Air pollution by chemical or bacteriological pollutants is not limited to a geographical area at the discrete border. Also, factors taken into account in the risk analysis such as patient age, seasons, socio-economic background or race are far from being necessary as discrete values. Often used analysis tools are limited to statistical analyzes. Given the nature of this data, imprecise and complex, in this study we propose an artificial intelligence tool, in particular the principles of fuzzy logic in data processing. A fuzzy system is constructed with five fuzzy input variables (Nature of the pollutant, geographic area, season,) and an output variable which expresses the corresponding health risk in terms of disease with its incidence rate. The rule base must contain all possible combinations. This tool can be used as a tool to aid in the prognosis and in the prevention of the onset of epidemiological diseases.

Automatic detection of cognitive impairment in elderly people using an entertainment chatbot with Natural Language Processing capabilities

Previous researchers have proposed intelligent systems for therapeutic monitoring of cognitive impairments. However, most existing practical approaches for this purpose are based on manual tests. This raises issues such as excessive caretaking effort and the white-coat effect. To avoid these issues, we present an intelligent conversational system for entertaining elderly people with news of their interest that monitors cognitive impairment transparently. Automatic chatbot dialogue stages allow assessing content description skills and detecting cognitive impairment with Machine Learning algorithms. We create these dialogue flows automatically from updated news items using Natural Language Generation techniques. The system also infers the gold standard of the answers to the questions, so it can assess cognitive capabilities automatically by comparing these answers with the user responses. It employs a similarity metric with values in [0, 1], in increasing level of similarity. To evaluate the performance and usability of our approach, we have conducted field tests with a test group of 30 elderly people in the earliest stages of dementia, under the supervision of gerontologists. In the experiments, we have analysed the effect of stress and concentration in these users. Those without cognitive impairment performed up to five times better. In particular, the similarity metric varied between 0.03, for stressed and unfocused participants, and 0.36, for relaxed and focused users. Finally, we developed a Machine Learning algorithm based on textual analysis features for automatic cognitive impairment detection, which attained accuracy, F-measure and recall levels above 80%. We have thus validated the automatic approach to detect cognitive impairment in elderly people based on entertainment content. The results suggest that the solution has strong potential for long-term user-friendly therapeutic monitoring of elderly people.

Exploiting machine learning for bestowing intelligence to microfluidics

Intelligent microfluidics is an emerging cross-discipline research area formed by combining microfluidics with machine learning. It uses the advantages of microfluidics, such as high throughput and controllability, and the powerful data processing capabilities of machine learning, resulting in improved systems in biotechnology and chemistry. Compared to traditional microfluidics using manual analysis methods, intelligent microfluidics needs less human intervention, and results in a more user-friendly experience with faster processing. There is a paucity of literature reviewing this burgeoning and highly promising cross-discipline. Therefore, we herein comprehensively and systematically summarize several aspects of microfluidic applications enabled by machine learning. We list the types of microfluidics used in intelligent microfluidic applications over the last five years, as well as the machine learning algorithms and the hardware used for training. We also present the most recent advances in key technologies, developments, challenges, and the emerging opportunities created by intelligent microfluidics.

Reflections on epistemological aspects of artificial intelligence during the COVID-19 pandemic

Artificial intelligence plays an important role and has been used by several countries as a health strategy in an attempt to understand, control and find a cure for the disease caused by Coronavirus. These intelligent systems can assist in accelerating the process of developing antivirals for Coronavirus and in predicting new variants of this virus. For this reason, much research on COVID-19 has been developed with the aim of contributing to new discoveries about the Coronavirus. However, there are some epistemological aspects about the use of AI in this pandemic period of Covid-19 that deserve to be discussed and need reflections. In this scenario, this article presents a reflection on the two epistemological aspects faced by the COVID-19 pandemic: (1) The epistemological aspect resulting from the use of patient data to fill the knowledge base of intelligent systems; (2) the epistemological problem arising from the dependence of health professionals on the results/diagnoses issued by intelligent systems. In addition, we present some epistemological challenges to be implemented in a pandemic period.

Factoring ethics in management algorithms for municipal information-analytical systems

The discourse on the ethics of artificial intelligence (AI) has generated a plethora of different conventions, principles and guidelines outlining an ethical perspective on the use and research of AI. However, when it comes to breaking down general implications to specific use cases, existent frameworks have been remaining vague. The following paper aims to fill this gap by examining the ethical implications of the use of information analytical systems through a management approach for filtering the content in social media and preventing information thrusts with negative consequences for human beings and public administration. The ethical dimensions of AI technologies are revealed through deduction of general challenges of digital governance to applied level management technics.

An optimal cascaded recurrent neural network for intelligent COVID-19 detection using Chest X-ray images

In recent times, COVID-19, has a great impact on the healthcare sector and results in a wide range of respiratory illnesses. It is a type of Ribonucleic acid (RNA) virus, which affects humans as well as animals. Though several artificial intelligence-based COVID-19 diagnosis models have been presented in the literature, most of the works have not focused on the hyperparameter tuning process. Therefore, this paper proposes an intelligent COVID-19 diagnosis model using a barnacle mating optimization (BMO) algorithm with a cascaded recurrent neural network (CRNN) model, named BMO-CRNN. The proposed BMO-CRNN model aims to detect and classify the existence of COVID-19 from Chest X-ray images. Initially, pre-processing is applied to enhance the quality of the image. Next, the CRNN model is used for feature extraction, followed by hyperparameter tuning of CRNN via the BMO algorithm to improve the classification performance. The BMO algorithm determines the optimal values of the CRNN hyperparameters namely learning rate, batch size, activation function, and epoch count. The application of CRNN and hyperparameter tuning using the BMO algorithm shows the novelty of this work. A comprehensive simulation analysis is carried out to ensure the better performance of the BMO-CRNN model, and the experimental outcome is investigated using several performance metrics. The simulation results portrayed that the BMO-CRNN model has showcased optimal performance with an average sensitivity of 97.01%, specificity of 98.15%, accuracy of 97.31%, and F-measure of 97.73% compared to state-of-the-art methods.

Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions

Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0). Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, text analytics, cybersecurity, and many more. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. This article presents a structured and comprehensive view on DL techniques including a taxonomy considering various types of real-world tasks like supervised or unsupervised. In our taxonomy, we take into account deep networks for supervised or discriminative learning, unsupervised or generative learning as well as hybrid learning and relevant others. We also summarize real-world application areas where deep learning techniques can be used. Finally, we point out ten potential aspects for future generation DL modeling with research directions. Overall, this article aims to draw a big picture on DL modeling that can be used as a reference guide for both academia and industry professionals.

Personalized training model for organizing blended and lifelong distance learning courses and its effectiveness in Higher Education

The main goal of this research is to improve the personification of learning in higher education. The proposed flexible model for organizing blended and distance learning in higher education involves the creation of an individual learning path through testing students before the start of training. Based on the learning outcomes, the student is credited to the learning path. The training path consists of mandatory and additional modules for training; additional modules can be skipped after successfully passing the test, without studying these modules. The paper examines the composition of intelligent learning systems: student model, learning model and interface model. A student model is described, which contains the level of their knowledge, skills and abilities, the ability to learn, the ability to complete tasks (whether they know how to use the information received or not), personal characteristics (type, orientation) and other factors. The student's model is described by a mathematical formula. Thus, being described using logical rules, which have formed the basis for the software implementation of mixed and distance learning rules for lifelong learning courses. There is an interface model presented in the paper, and the results of the course of the proposed flexible model for the organization of mixed and distance learning "Digital Skills of a Modern Teacher in the Context of Distance Learning", as well as the face-to-face course "Digital Learning for Everyone" before the start of the pandemic which is close in its content to the course under study. Based on the results of the analysis, we introduced criteria for the effectiveness of the training course, proposed the weighting coefficients for evaluating the training course, carried out the assessment and drew conclusions.

Intelligent systems for additive manufacturing-based repair in remanufacturing: a systematic review of its potential

The conventional component repair in remanufacturing involves human decision making that is influenced by several factors such as conditions of incoming cores, modes of failure, severity of damage, features and geometric complexities of cores and types of reparation required. Repair can be enhanced through automation using additive manufacturing (AM) technology. Advancements in AM have led to the development of directed energy deposition and laser cladding technology for repair of damaged parts and components. The objective of this systematic literature review is to ascertain how intelligent systems can be integrated into AM-based repair, through artificial intelligence (AI) approaches capable of supporting the nature and process of decision making during repair. The integration of intelligent systems in AM repair is expected to enhance resource utilization and repair efficiency during remanufacturing. Based on a systematic literature review of articles published during 2005-2021, the study analyses the activities of conventional repair in remanufacturing, trends in the applications of AM for repair using the current state-of-the-art technology and how AI has been deployed to facilitate repair. The study concludes with suggestions on research areas and opportunities that will further enhance the automation of component repair during remanufacturing using intelligent AM systems.

Toward development of PreVoid alerting system for nocturnal enuresis patients: A fuzzy-based approach for determining the level of liquid encased in urinary bladder

Preventive and accurate assessment of bladder voiding dysfunctions necessitates measuring the amount of liquid encapsulated within urinary bladder walls in a non-invasive and real-time manner. The real-time monitoring of urine levels helps patients with urological disorders such as Nocturnal Enuresis (NE) by preventing the occurrence of enuresis via a pre-void stage alerting system. Although some advances have been achieved toward developing a non-invasive approach for determining the amount of accumulated urine inside the bladder, there is still a lack of an easy-to-implement technique which is suitable to embed in a wearable pre-warning device. This study aims to develop a machine-learning empowered technique to quantify to what extent an individual's bladder is filled by observing the filling-voiding pattern of a patient over a training period. In this experiment, a pulse-echo sonar element is used to generate ultrasound pulses while the probe surface is positioned perpendicular to the bladder's position. From the reflected echoes, four features which show sufficient sensitiveness and therefore could be modulated noticeably by different levels of liquid encased in the bladder, are extracted. The extracted features are then fed into a novel intelligent decision support system- known as FECOC - which is based on hybridization of fuzzy inference systems (FIS) and error correcting output codes (ECOC). The proposed scheme tends to achieve better results when examined in real case studies.

Advances in biomimetic stimuli responsive soft grippers

A variety of biomimetic stimuli-responsive soft grippers that can be utilized as intelligent actuators, sensors, or biomedical tools have been developed. This review covers stimuli-responsive materials, fabrication methods, and applications of soft grippers. This review specifically describes the current research progress in stimuli-responsive grippers composed of N-isopropylacrylamide hydrogel, thermal and light-responding liquid crystalline and/or pneumatic-driven shape-morphing elastomers. Furthermore, this article provides a brief overview of high-throughput assembly methods, such as photolithography and direct printing approaches, to create stimuli-responsive soft grippers. This review primarily focuses on stimuli-responsive soft gripping robots that can be utilized as tethered/untethered multiscale smart soft actuators, manipulators, or biomedical devices.