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A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Computational Neuroscience and Neuroinformatics".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 17782

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Special Issue Editors

Dr. Habib Shah

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Guest Editor

College of Computer Science, King Khalid University, Abha, Saudi Arabia
Interests: computational intelligente; advanced machine learning; pattern recognition; applied artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Danish Shehzad

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Guest Editor

Department of Computer Science, The Superior University, Lahore, Pakistan
Interests: network; computer networking; computer networks security; network security; federated learning and malware analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Technology is becoming more entrenched in our daily lives along with each passing minute, which is rapidly changing the world and human needs and living standards. One of the most important areas which is amazingly contributing in the advancement of technological world is Artificial Intelligence with their sub fields: Machine Learning, Neural Networks, Deep Learning and other intelligent agents and techniques. Especially the machine learning and neural networks methods are the backbone and strength of the Deep Learning algorithms.

Artificial Intelligence (AI) is a science devoted to making machines think and act like humans in the complex areas ranging from applied to real world problems. Broadly, AI describes when a machine mimics cognitive functions that humans associate with other human minds, such as learning and problem solving. On an even more elementary level, AI can merely be a programmed rule that tells the machine to behave in a specific way in certain situations. Machine learning is about computers being able to think and act with less human intervention; deep learning is about computers learning to think using structures modeled on the human brain.

There are various techniques of improving these methods such as hybridization with Bio-Inspired Learning algorithms have attracted the researchers from various fields like computer science, mathematics and engineering as well. Moreover, soon there will be no industry left where AI is not being used. Few of the most common AI applications that will have a great impact on human lives include Education powered by AI, autonomous transportation, entertainment with AI, E-health, home and service robotics, predictive policing and space exploration with AI.

With this Special Issue we aim to understand in a better way the difference between those technology and how they could impact the human brain. We are soliciting empirical articles, theoretical proposals, and scientific reviews investigating the effects of innovative interventions of deep learning, machine learning and Neural Networks. Potential topics include, but are not limited to, the following:

Artificial Neural Networks with various Types
Machine Learning Algorithm various Types
Hybrid Bio-Inspired learning Algorithms
Deep Learning Algorithms
Time Series and Boolean Data classification/clustering /prediction
Natural Disasters/ Industrial/Geological events
Complex Numerical Function Optimization
Internet of Things, Big Data or Cyber Data set etc.
Smart Cities projects with Multi-agent Systems
Diverse Application areas of AI.

You may choose our Joint Special Issue in Algorithms.

Dr. Habib Shah
Dr. Danish Shehzad
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

neural networks
machine learning
deep learning
Artificial Intelligence (AI)
cognitive functions
human brain
federated learning and malware analysis

Published Papers (7 papers)

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Research

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21 pages, 4511 KiB

Open AccessArticle

Attention Aware Deep Learning Approaches for an Efficient Stress Classification Model

by Muhammad Zulqarnain, Habib Shah, Rozaida Ghazali, Omar Alqahtani, Rubab Sheikh and Muhammad Asadullah

Brain Sci. 2023, 13(7), 994; https://doi.org/10.3390/brainsci13070994 - 25 Jun 2023

Viewed by 1151

Abstract

In today’s world, stress is a major factor for various diseases in modern societies which affects the day-to-day activities of human beings. The measurement of stress is a contributing factor for governments and societies that impacts the quality of daily lives. The strategy of stress monitoring systems requires an accurate stress classification technique which is identified via the reactions of the body to regulate itself to changes within the environment through mental and emotional responses. Therefore, this research proposed a novel deep learning approach for the stress classification system. In this paper, we presented an Enhanced Long Short-Term Memory(E-LSTM) based on the feature attention mechanism that focuses on determining and categorizing the stress polarity using sequential modeling and word-feature seizing. The proposed approach integrates pre-feature attention in E-LSTM to identify the complicated relationship and extract the keywords through an attention layer for stress classification. This research has been evaluated using a selected dataset accessed from the sixth Korea National Health and Nutrition Examination Survey conducted from 2013 to 2015 (KNHANES VI) to analyze health-related stress data. Statistical performance of the developed approach was analyzed based on the nine features of stress detection, and we compared the effectiveness of the developed approach with other different stress classification approaches. The experimental results shown that the developed approach obtained accuracy, precision, recall and a F1-score of 75.54%, 74.26%, 72.99% and 74.58%, respectively. The feature attention mechanism-based E-LSTM approach demonstrated superior performance in stress detection classification when compared to other classification methods including naïve Bayesian, SVM, deep belief network, and standard LSTM. The results of this study demonstrated the efficiency of the proposed approach in accurately classifying stress detection, particularly in stress monitoring systems where it is expected to be effective for stress prediction. Full article

(This article belongs to the Special Issue Intelligent Neural Systems for Solving Real Problems)

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18 pages, 5565 KiB

Open AccessEditor’s ChoiceArticle

Automated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning

by Zahid Rasheed, Yong-Kui Ma, Inam Ullah, Tamara Al Shloul, Ahsan Bin Tufail, Yazeed Yasin Ghadi, Muhammad Zubair Khan and Heba G. Mohamed

Brain Sci. 2023, 13(4), 602; https://doi.org/10.3390/brainsci13040602 - 01 Apr 2023

Cited by 7 | Viewed by 1788

Abstract

Brain tumor classification is crucial for medical evaluation in computer-assisted diagnostics (CAD). However, manual diagnosis of brain tumors from magnetic resonance imaging (MRI) can be time-consuming and complex, leading to inaccurate detection and classification. This is mainly because brain tumor identification is a complex procedure that relies on different modules. The advancements in Deep Learning (DL) have assisted in the automated process of medical images and diagnostics for various medical conditions, which benefits the health sector. Convolutional Neural Network (CNN) is one of the most prominent DL methods for visual learning and image classification tasks. This study presents a novel CNN algorithm to classify the brain tumor types of glioma, meningioma, and pituitary. The algorithm was tested on benchmarked data and compared with the existing pre-trained VGG16, VGG19, ResNet50, MobileNetV2, and InceptionV3 algorithms reported in the literature. The experimental results have indicated a high classification accuracy of 98.04%, precision, recall, and f1-score success rate of 98%, respectively. The classification results proved that the most common kinds of brain tumors could be categorized with a high level of accuracy. The presented algorithm has good generalization capability and execution speed that can be helpful in the field of medicine to assist doctors in making prompt and accurate decisions associated with brain tumor diagnosis. Full article

(This article belongs to the Special Issue Intelligent Neural Systems for Solving Real Problems)

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21 pages, 3794 KiB

Open AccessArticle

Lie Recognition with Multi-Modal Spatial–Temporal State Transition Patterns Based on Hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory

by Sunusi Bala Abdullahi, Zakariyya Abdullahi Bature, Lubna A. Gabralla and Haruna Chiroma

Brain Sci. 2023, 13(4), 555; https://doi.org/10.3390/brainsci13040555 - 25 Mar 2023

Cited by 5 | Viewed by 1580

Abstract

Recognition of lying is a more complex cognitive process than truth-telling because of the presence of involuntary cognitive cues that are useful to lie recognition. Researchers have proposed different approaches in the literature to solve the problem of lie recognition from either handcrafted and/or automatic lie features during court trials and police interrogations. Unfortunately, due to the cognitive complexity and the lack of involuntary cues related to lying features, the performances of these approaches suffer and their generalization ability is limited. To improve performance, this study proposed state transition patterns based on hands, body motions, and eye blinking features from real-life court trial videos. Each video frame is represented according to a computed threshold value among neighboring pixels to extract spatial–temporal state transition patterns (STSTP) of the hand and face poses as involuntary cues using fully connected convolution neural network layers optimized with the weights of ResNet-152 learning. In addition, this study computed an eye aspect ratio model to obtain eye blinking features. These features were fused together as a single multi-modal STSTP feature model. The model was built using the enhanced calculated weight of bidirectional long short-term memory. The proposed approach was evaluated by comparing its performance with current state-of-the-art methods. It was found that the proposed approach improves the performance of detecting lies. Full article

(This article belongs to the Special Issue Intelligent Neural Systems for Solving Real Problems)

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17 pages, 3382 KiB

Open AccessArticle

Detecting Cortical Thickness Changes in Epileptogenic Lesions Using Machine Learning

by Sumayya Azzony, Kawthar Moria and Jamaan Alghamdi

Brain Sci. 2023, 13(3), 487; https://doi.org/10.3390/brainsci13030487 - 14 Mar 2023

Cited by 2 | Viewed by 2689

Abstract

Epilepsy is a neurological disorder characterized by abnormal brain activity. Epileptic patients suffer from unpredictable seizures, which may cause a loss of awareness. Seizures are considered drug resistant if treatment does not affect success. This leads practitioners to calculate the cortical thickness to measure the distance between the brain’s white and grey matter surfaces at various locations to perform a surgical intervention. In this study, we introduce using machine learning as an approach to classify extracted measurements from T1-weighted magnetic resonance imaging. Data were collected from the epilepsy unit at King Abdulaziz University Hospital. We applied two trials to classify the extracted measurements from T1-weighted MRI for drug-resistant epilepsy and healthy control subjects. The preprocessing sequence on T1-weighted MRI images was performed using C++ through BrainSuite’s pipeline. The first trial was performed on seven different combinations of four commonly selected measurements. The best performance was achieved in Exp6 and Exp7, with 80.00% accuracy, 83.00% recall score, and 83.88% precision. It is noticeable that grey matter volume and white matter volume measurements are more significant than the cortical thickness measurement. The second trial applied four different machine learning classifiers after applying 10-fold cross-validation and principal component analysis on all extracted measurements as in the first trial based on the mentioned previous works. The K-nearest neighbours model outperformed the other machine learning classifiers with 97.11% accuracy, 75.00% recall score, and 75.00% precision. Full article

(This article belongs to the Special Issue Intelligent Neural Systems for Solving Real Problems)

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12 pages, 1614 KiB

Open AccessArticle

Investigating Students’ Pre-University Admission Requirements and Their Correlation with Academic Performance for Medical Students: An Educational Data Mining Approach

by Ayman Qahmash, Naim Ahmad and Abdulmohsen Algarni

Brain Sci. 2023, 13(3), 456; https://doi.org/10.3390/brainsci13030456 - 08 Mar 2023

Cited by 1 | Viewed by 1454

Abstract

Medical education is one of the most sought-after disciplines for its prestigious and noble status. Institutions endeavor to identify admissions criteria to register bright students who can handle the complexity of medical training and become competent clinicians. This study aims to apply statistical and educational data mining approaches to study the relationship between pre-admission criteria and student performance in medical programs at a public university in Saudi Arabia. The present study is a retrospective cohort study conducted at the College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia between February and November 2022. The current pre-admission criterion is the admission score taken as the weighted average of high school percentage (HSP), general aptitude test (GAT) and standard achievement admission test (SAAT), with respective weights of 0.3, 0.3 and 0.4. Regression and optimization techniques have been applied to identify weightages that better fit the data. Five classification techniques—Decision Tree, Neural Network, Random Forest, Naïve Bayes and K-Nearest Neighbors—are employed to develop models to predict student performance. The regression and optimization analyses show that optimized weights of HSP, GAT and SAAT are 0.3, 0.2 and 0.5, respectively. The results depict that the performance of the models improves with admission scores based on optimized weightages. Further, the Neural Network and Naïve Bayes techniques outperform other techniques. Firstly, this study proposes to revise the weights of HSP, GAT and SAAT to 0.3, 0.2 and 0.5, respectively. Secondly, as the evaluation metrics of models remain less than 0.75, this study proposes to identify additional student features for calculating admission scores to select ideal candidates for medical programs. Full article

(This article belongs to the Special Issue Intelligent Neural Systems for Solving Real Problems)

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20 pages, 3117 KiB

Open AccessArticle

Using the AraBERT Model for Customer Satisfaction Classification of Telecom Sectors in Saudi Arabia

by Sulaiman Aftan and Habib Shah

Brain Sci. 2023, 13(1), 147; https://doi.org/10.3390/brainsci13010147 - 14 Jan 2023

Cited by 3 | Viewed by 2464

Abstract

Customer satisfaction and loyalty are essential for every business. Feedback prediction and social media classification are crucial and play a key role in accurately identifying customer satisfaction. This paper presents sentiment analysis-based customer feedback prediction based on Twitter Arabic datasets of telecommunications companies in Saudi Arabia. The human brain, which contains billions of neurons, provides feedback based on the current and past experience provided by the services and other related stakeholders. Artificial Intelligent (AI) based methods, parallel to human brain processing methods such as Deep Learning (DL) algorithms, are famous for classifying and analyzing such datasets. Comparing the Arabic Dataset to English, it is pretty challenging for typical methods to outperform in the classification or prediction tasks. Therefore, the Arabic Bidirectional Encoder Representations from Transformers (AraBERT) model was used and analyzed with various parameters such as activation functions and topologies and simulated customer satisfaction prediction takes using Arabic Twitter datasets. The prediction results were compared with two famous DL algorithms: Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Results show that these methods have been successfully applied and obtained highly accurate classification results. AraBERT achieved the best prediction accuracy among the three ML methods, especially with Mobily and STC datasets. Full article

(This article belongs to the Special Issue Intelligent Neural Systems for Solving Real Problems)

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Review

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30 pages, 2821 KiB

Open AccessReview

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

by Tehseen Mazhar, Dhani Bux Talpur, Tamara Al Shloul, Yazeed Yasin Ghadi, Inayatul Haq, Inam Ullah, Khmaies Ouahada and Habib Hamam

Brain Sci. 2023, 13(4), 683; https://doi.org/10.3390/brainsci13040683 - 19 Apr 2023

Cited by 11 | Viewed by 4965

Abstract

The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system. IoT security intelligence is examined in this paper from every angle available. An innovative method for protecting IoT devices against a variety of cyberattacks is to use machine learning and deep learning to gain information from raw data. Finally, we discuss relevant research issues and potential next steps considering our findings. This article examines how machine learning and deep learning can be used to detect attack patterns in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as well as potential future directions for this research area, considering these findings. Anyone with an interest in the IoT or cybersecurity can use this website’s content as a technical resource and reference. Full article

(This article belongs to the Special Issue Intelligent Neural Systems for Solving Real Problems)

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