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10th Anniversary of Electronics: Recent Advances in Artificial Intelligence
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10th Anniversary of Electronics: Recent Advances in Artificial Intelligence

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 40495

Special Issue Editors

Prof. Dr. Yoichi Hayashi

E-Mail Website1 Website2 Website3
Guest Editor
Artificial Intelligence Lab, Department of Computer Science, Meiji University, Kawasaki, Kanagawa 214-8571, Japan
Interests: artificial intelligence; knowledge extraction; rule extraction; transparency of deep learning neural networks; medical informatics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Debjyoti Banerjee

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843-3123, USA
Interests: thermo-fluidics; nanofluids; nano-calorimeter; phase change materials; energy storage; solar power; numerical simulations; soft computing; artificial intelligence; machine learning
Prof. Dr. Hideaki IIduka

E-Mail Website
Guest Editor
Department of Computer Science, Meiji University, Kawasaki, Kanagawa 214-8571, Japan
Interests: continuous optimization; machine learning; network resource allocation

Special Issue Information

Dear Colleagues,

It has now been ten years since the first paper was published in Electronics back in 2012. It has been a rocky road with many highs and many lows, but we are extremely proud to have reached this very important milestone of the 10th anniversary of the journal. To celebrate this momentous occasion, a Special Issue is being prepared which invites both members of the Editorial Board and outstanding renowned authors, including past editors and authors, to submit their high-quality works on the topic of "Artificial Intelligence".

Prof. Dr. Yoichi Hayashi
Prof. Dr. Debjyoti Banerjee
Prof. Dr. Hideaki IIduka
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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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.

Published Papers (7 papers)

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Research

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35 pages, 4329 KiB  
Article
Hybridizing of Whale and Moth-Flame Optimization Algorithms to Solve Diverse Scales of Optimal Power Flow Problem
by Mohammad H. Nadimi-Shahraki, Ali Fatahi, Hoda Zamani, Seyedali Mirjalili and Diego Oliva
Electronics 2022, 11(5), 831; https://doi.org/10.3390/electronics11050831 - 07 Mar 2022
Cited by 42 | Viewed by 2597
Abstract
The optimal power flow (OPF) is a practical problem in a power system with complex characteristics such as a large number of control parameters and also multi-modal and non-convex objective functions with inequality and nonlinear constraints. Thus, tackling the OPF problem is becoming [...] Read more.
The optimal power flow (OPF) is a practical problem in a power system with complex characteristics such as a large number of control parameters and also multi-modal and non-convex objective functions with inequality and nonlinear constraints. Thus, tackling the OPF problem is becoming a major priority for power engineers and researchers. Many metaheuristic algorithms with different search strategies have been developed to solve the OPF problem. Although, the majority of them suffer from stagnation, premature convergence, and local optima trapping during the optimization process, which results in producing low solution qualities, especially for real-world problems. This study is devoted to proposing an effective hybridizing of whale optimization algorithm (WOA) and a modified moth-flame optimization algorithm (MFO) named WMFO to solve the OPF problem. In the proposed WMFO, the WOA and the modified MFO cooperate to effectively discover the promising areas and provide high-quality solutions. A randomized boundary handling is used to return the solutions that have violated the permissible boundaries of search space. Moreover, a greedy selection operator is defined to assess the acceptance criteria of new solutions. Ultimately, the performance of the WMFO is scrutinized on single and multi-objective cases of different OPF problems including standard IEEE 14-bus, IEEE 30-bus, IEEE 39-bus, IEEE 57-bus, and IEEE118-bus test systems. The obtained results corroborate that the proposed algorithm outperforms the contender algorithms for solving the OPF problem. Full article
(This article belongs to the Special Issue 10th Anniversary of Electronics: Recent Advances in Artificial Intelligence)
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25 pages, 11355 KiB  
Article
Machine Learning (ML) Based Thermal Management for Cooling of Electronics Chips by Utilizing Thermal Energy Storage (TES) in Packaging That Leverages Phase Change Materials (PCM)
by Aditya Chuttar and Debjyoti Banerjee
Electronics 2021, 10(22), 2785; https://doi.org/10.3390/electronics10222785 - 13 Nov 2021
Cited by 8 | Viewed by 3772
Abstract
Miniaturization of electronics devices is often limited by the concomitant high heat fluxes (cooling load) and maldistribution of temperature profiles (hot spots). Thermal energy storage (TES) platforms providing supplemental cooling can be a cost-effective solution, that often leverages phase change materials (PCM). Although [...] Read more.
Miniaturization of electronics devices is often limited by the concomitant high heat fluxes (cooling load) and maldistribution of temperature profiles (hot spots). Thermal energy storage (TES) platforms providing supplemental cooling can be a cost-effective solution, that often leverages phase change materials (PCM). Although salt hydrates provide higher storage capacities and power ratings (as compared to that of the organic PCMs), they suffer from reliability issues (e.g., supercooling). “Cold Finger Technique (CFT)” can obviate supercooling by maintaining a small mass fraction of the PCM in a solid state for enabling spontaneous nucleation. Optimization of CFT necessitates real-time forecasting of the transient values of the melt-fraction. In this study, the artificial neural network (ANN) is explored for real-time prediction of the time remaining to reach a target value of melt-fraction based on the prior history of the spatial distribution of the surface temperature transients. Two different approaches were explored for training the ANN model, using: (1) transient PCM-temperature data; or (2) transient surface-temperature data. When deployed in a heat sink that leverages PCM-based passive thermal management systems for cooling electronic chips and packages, this maverick approach (using the second method) affords cheaper costs, better sustainability, higher reliability, and resilience. The error in prediction varies during the melting process. During the final stages of the melting cycle, the errors in the predicted values are ~5% of the total time-scale of the PCM melting experiments. Full article
(This article belongs to the Special Issue 10th Anniversary of Electronics: Recent Advances in Artificial Intelligence)
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10 pages, 1491 KiB  
Article
Bioinspired Auditory Model for Vowel Recognition
by Viviana Abad Peraza, José Manuel Ferrández Vicente and Ernesto Arturo Martínez Rams
Electronics 2021, 10(18), 2304; https://doi.org/10.3390/electronics10182304 - 18 Sep 2021
Cited by 1 | Viewed by 1409
Abstract
In this work, a bioinspired or neuromorphic model to replicate the vowel recognition process for an auditory system is presented. A bioinspired peripheral and central auditory system model is implemented and a neuromorphic higher auditory system model based on artificial neuronal nets for [...] Read more.
In this work, a bioinspired or neuromorphic model to replicate the vowel recognition process for an auditory system is presented. A bioinspired peripheral and central auditory system model is implemented and a neuromorphic higher auditory system model based on artificial neuronal nets for vowel recognition is proposed. For their verification, ten Hispanic Spanish language-speaking adults (five males and five females) were used. With the proposed bioinspired model based on artificial neuronal nets it is possible to recognize with high levels of accuracy and sensibility the vowels phonemes of speech signals and the assessment of cochlear implant stimulation strategies in terms of vowel recognition. Full article
(This article belongs to the Special Issue 10th Anniversary of Electronics: Recent Advances in Artificial Intelligence)
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19 pages, 966 KiB  
Article
DeepSGP: Deep Learning for Gene Selection and Survival Group Prediction in Glioblastoma
by Ritaban Kirtania, Subhashis Banerjee, Sayantan Laha, B. Uma Shankar, Raghunath Chatterjee and Sushmita Mitra
Electronics 2021, 10(12), 1463; https://doi.org/10.3390/electronics10121463 - 18 Jun 2021
Cited by 1 | Viewed by 2068
Abstract
Glioblastoma Multiforme (GBM) is an aggressive form of glioma, exhibiting very poor survival. Genomic input, in the form of RNA sequencing data (RNA-seq), is expected to provide vital information about the characteristics of the genes that affect the Overall Survival (OS) of patients. [...] Read more.
Glioblastoma Multiforme (GBM) is an aggressive form of glioma, exhibiting very poor survival. Genomic input, in the form of RNA sequencing data (RNA-seq), is expected to provide vital information about the characteristics of the genes that affect the Overall Survival (OS) of patients. This could have a significant impact on treatment planning. We present a new Autoencoder (AE)-based strategy for the prediction of survival (low or high) of GBM patients, using the RNA-seq data of 129 GBM samples from The Cancer Genome Atlas (TCGA). This is a novel interdisciplinary approach to integrating genomics with deep learning towards survival prediction. First, the Differentially Expressed Genes (DEGs) were selected using EdgeR. These were further reduced using correlation-based analysis. This was followed by the application of ranking with different feature subset selection and feature extraction algorithms, including the AE. In each case, fifty features were selected/extracted, for subsequent prediction with different classifiers. An exhaustive study for survival group prediction, using eight different classifiers with the accuracy and Area Under the Curve (AUC), established the superiority of the AE-based feature extraction method, called DeepSGP. It produced a very high accuracy (0.83) and AUC (0.90). Of the eight classifiers, using the extracted features by DeepSGP, the MLP was the best at Overall Survival (OS) prediction with an accuracy of 0.89 and an AUC of 0.97. The biological significance of the genes extracted by the AE were also analyzed to establish their importance. Finally, the statistical significance of the predicted output of the DeepSGP algorithm was established using the concordance index. Full article
(This article belongs to the Special Issue 10th Anniversary of Electronics: Recent Advances in Artificial Intelligence)
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22 pages, 729 KiB  
Article
Automated Classification of Mental Arithmetic Tasks Using Recurrent Neural Network and Entropy Features Obtained from Multi-Channel EEG Signals
by Abhishek Varshney, Samit Kumar Ghosh, Sibasankar Padhy, Rajesh Kumar Tripathy and U. Rajendra Acharya
Electronics 2021, 10(9), 1079; https://doi.org/10.3390/electronics10091079 - 02 May 2021
Cited by 29 | Viewed by 3998
Abstract
The automated classification of cognitive workload tasks based on the analysis of multi-channel EEG signals is vital for human–computer interface (HCI) applications. In this paper, we propose a computerized approach for categorizing mental-arithmetic-based cognitive workload tasks using multi-channel electroencephalogram (EEG) signals. The approach [...] Read more.
The automated classification of cognitive workload tasks based on the analysis of multi-channel EEG signals is vital for human–computer interface (HCI) applications. In this paper, we propose a computerized approach for categorizing mental-arithmetic-based cognitive workload tasks using multi-channel electroencephalogram (EEG) signals. The approach evaluates various entropy features, such as the approximation entropy, sample entropy, permutation entropy, dispersion entropy, and slope entropy, from each channel of the EEG signal. These features were fed to various recurrent neural network (RNN) models, such as long-short term memory (LSTM), bidirectional LSTM (BLSTM), and gated recurrent unit (GRU), for the automated classification of mental-arithmetic-based cognitive workload tasks. Two cognitive workload classification strategies (bad mental arithmetic calculation (BMAC) vs. good mental arithmetic calculation (GMAC); and before mental arithmetic calculation (BFMAC) vs. during mental arithmetic calculation (DMAC)) are considered in this work. The approach was evaluated using the publicly available mental arithmetic task-based EEG database. The results reveal that our proposed approach obtained classification accuracy values of 99.81%, 99.43%, and 99.81%, using the LSTM, BLSTM, and GRU-based RNN classifiers, respectively for the BMAC vs. GMAC cognitive workload classification strategy using all entropy features and a 10-fold cross-validation (CV) technique. The slope entropy features combined with each RNN-based model obtained higher classification accuracy compared with other entropy features for the classification of the BMAC vs. GMAC task. We obtained the average classification accuracy values of 99.39%, 99.44%, and 99.63% for the classification of the BFMAC vs. DMAC tasks, using the LSTM, BLSTM, and GRU classifiers with all entropy features and a hold-out CV scheme. Our developed automated mental arithmetic task system is ready to be tested with more databases for real-world applications. Full article
(This article belongs to the Special Issue 10th Anniversary of Electronics: Recent Advances in Artificial Intelligence)
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15 pages, 12724 KiB  
Article
An Efficient Management Platform for Developing Smart Cities: Solution for Real-Time and Future Crowd Detection
by David Garcia-Retuerta, Pablo Chamoso, Guillermo Hernández, Agustín San Román Guzmán, Tan Yigitcanlar and Juan M. Corchado
Electronics 2021, 10(7), 765; https://doi.org/10.3390/electronics10070765 - 24 Mar 2021
Cited by 24 | Viewed by 3112
Abstract
A smart city is an environment that uses innovative technologies to make networks and services more flexible, effective, and sustainable with the use of information, digital, and telecommunication technologies, improving the city’s operations for the benefit of its citizens. Most cities incorporate data [...] Read more.
A smart city is an environment that uses innovative technologies to make networks and services more flexible, effective, and sustainable with the use of information, digital, and telecommunication technologies, improving the city’s operations for the benefit of its citizens. Most cities incorporate data acquisition elements from their own systems or those managed by subcontracted companies that can be used to optimise their resources: energy consumption, smart meters, lighting, irrigation water consumption, traffic data, camera images, waste collection, security systems, pollution meters, climate data, etc. The city-as-a-platform concept is becoming popular and it is increasingly evident that cities must have efficient management systems capable of deploying, for instance, IoT platforms, open data, etc., and of using artificial intelligence intensively. For many cities, data collection is not a problem, but managing and analysing data with the aim of optimising resources and improving the lives of citizens is. This article presents deepint.net, a platform for capturing, integrating, analysing, and creating dashboards, alert systems, optimisation models, etc. This article shows how deepint.net has been used to estimate pedestrian traffic on the streets of Melbourne (Australia) using the XGBoost algorithm. Given the current situation, it is advisable not to transit urban roads when overcrowded, thus, the model proposed in this paper (and implemented with deepint.net) facilitates the identification of areas with less pedestrian traffic. This use case is an example of an efficient crowd management system, implemented and operated via a platform that offers many possibilities for the management of the data collected in smart territories and cities. Full article
(This article belongs to the Special Issue 10th Anniversary of Electronics: Recent Advances in Artificial Intelligence)
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Review

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43 pages, 5869 KiB  
Review
Artificial Neural Networks Based Optimization Techniques: A Review
by Maher G. M. Abdolrasol, S. M. Suhail Hussain, Taha Selim Ustun, Mahidur R. Sarker, Mahammad A. Hannan, Ramizi Mohamed, Jamal Abd Ali, Saad Mekhilef and Abdalrhman Milad
Electronics 2021, 10(21), 2689; https://doi.org/10.3390/electronics10212689 - 03 Nov 2021
Cited by 152 | Viewed by 22406
Abstract
In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., [...] Read more.
In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system. Full article
(This article belongs to the Special Issue 10th Anniversary of Electronics: Recent Advances in Artificial Intelligence)
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