<abstract><title style='display:none'>Abstract</title>Deep Neural Networks (DNN) are nothing but neural networks with many hidden layers. DNNs are becoming popular in automatic speech recognition tasks which combines a good acoustic with a language model. Standard feedforward neural networks cannot handle speech data well since they do not have a way to feed information from a later layer back to an earlier layer. Thus, Recurrent Neural Networks (RNNs) have been introduced to take temporal dependencies into account. However, the shortcoming of RNNs is that long-term dependencies due to the vanishing/exploding gradient problem cannot be handled. Therefore, Long Short-Term Memory (LSTM) networks were introduced, which are a special case of RNNs, that takes long-term dependencies in a speech in addition to short-term dependencies into account. Similarily, GRU (Gated Recurrent Unit) networks are an improvement of LSTM networks also taking long-term dependencies into consideration. Thus, in this paper, we evaluate RNN, LSTM, and GRU to compare their performances on a reduced TED-LIUM speech data set. The results show that LSTM achieves the best word error rates, however, the GRU optimization is faster while achieving word error rates close to LSTM.</abstract>

Performance Evaluation of Deep Neural Networks Applied to Speech Recognition: RNN, LSTM and GRU

<abstract><title style='display:none'>Abstract</title>Music has the ability to evoke different emotions in people, which is reflected in their physiological signals. Advances in affective computing have introduced computational methods to analyse these signals and understand the relationship between music and emotion in greater detail. We analyse Electrodermal Activity (EDA), Blood Volume Pulse (BVP), Skin Temperature (ST) and Pupil Dilation (PD) collected from 24 participants while they listen to 12 pieces from 3 different genres of music. A set of 34 features were extracted from each signal and 6 different feature selection methods were applied to identify useful features. Empirical analysis shows that a neural network (NN) with a set of features extracted from the physiological signals can achieve 99.2% accuracy in differentiating among the 3 music genres. The model also reaches 98.5% accuracy in classification based on participants’ subjective rating of emotion. The paper also identifies some useful features to improve accuracy of the classification models. Furthermore, we introduce a new technique called ’Gingerbread Animation’ to visualise the physiological signals we record as a video, and to make these signals more comprehensible to the human eye, and also appropriate for computer vision techniques such as Convolutional Neural Networks (CNNs). Our results overall provide a strong motivation to investigate the relationship between physiological signals and music, which can lead to improvements in music therapy for mental health care and musicogenic epilepsy reduction (our long term goal).</abstract>

Towards Effective Music Therapy for Mental Health Care Using Machine Learning Tools: Human Affective Reasoning and Music Genres

<abstract><title style='display:none'>Abstract</title>There is a growing interest in applying machine learning algorithms to real-world examples by explicitly deriving models based on probabilistic reasoning. Sports analytics, being favoured mostly by the statistics community and less discussed in the machine learning community, becomes our focus in this paper. Specifically, we model two-team sports for the sake of one-match-ahead forecasting. We present a pioneering modeling approach based on stacked Bayesian regressions, in a way that winning probability can be calculated analytically. Benefiting from regression flexibility and high standard of performance, Sparse Spectrum Gaussian Process Regression (SSGPR) – an improved algorithm for the standard Gaussian Process Regression (GPR), was used to solve Bayesian regression tasks, resulting in a novel predictive model called TLGProb. For evaluation, TLGProb was applied to a popular sports event – National Basketball Association (NBA). Finally, 85.28% of the matches in NBA 2014/2015 regular season were correctly predicted by TLGProb, surpassing the existing predictive models for NBA.</abstract>

One-Match-Ahead Forecasting in Two-Team Sports with Stacked Bayesian Regressions

<abstract><title style='display:none'>Abstract</title>Computational intelligence firmly made its way into the areas of consumer applications, banking, education, social networks, and security. Among all the applications, biometric systems play a significant role in ensuring an uncompromised and secure access to resources and facilities. This article presents a first multimodal biometric system that combines KINECT gait modality with KINECT face modality utilizing the rank level and the score level fusion. For the KINECT gait modality, a new approach is proposed based on the skeletal information processing. The gait cycle is calculated using three consecutive local minima computed for the distance between left and right ankles. The feature distance vectors are calculated for each person’s gait cycle, which allows extracting the biometric features such as the mean and the variance of the feature distance vector. For Kinect face recognition, a novel method based on HOG features has been developed. Then, K-nearest neighbors feature matching algorithm is applied as feature classification for both gait and face biometrics. Two fusion algorithms are implemented. The combination of Borda count and logistic regression approaches are used in the rank level fusion. The weighted sum method is used for score level fusion. The recognition accuracy obtained for multi-modal biometric recognition system tested on KINECT Gait and KINECT Eurocom Face datasets is 93.33% for Borda count rank level fusion, 96.67% for logistic regression rank-level fusion and 96.6% for score level fusion.</abstract>

Score Level and Rank Level Fusion for Kinect-Based Multi-Modal Biometric System

<abstract><title style='display:none'>Abstract</title>The present article reviews the application of Particle Swarm Optimization (PSO) algorithms to optimize a phrasing model, which splits any text into linguistically-motivated phrases. In terms of its functionality, this phrasing model is equivalent to a shallow parser. The phrasing model combines attractive and repulsive forces between neighbouring words in a sentence to determine which segmentation points are required. The extrapolation of phrases in the specific application is aimed towards the automatic translation of unconstrained text from a source language to a target language via a phrase-based system, and thus the phrasing needs to be accurate and consistent to the training data.Experimental results indicate that PSO is effective in optimising the weights of the proposed parser system, using two different variants, namely sPSO and AdPSO. These variants result in statistically significant improvements over earlier phrasing results. An analysis of the experimental results leads to a proposed modification in the PSO algorithm, to prevent the swarm from stagnation, by improving the handling of the velocity component of particles. This modification results in more effective training sequences where the search for new solutions is extended in comparison to the basic PSO algorithm. As a consequence, further improvements are achieved in the accuracy of the phrasing module.</abstract>

Using Particle Swarm Optimization to Accurately Identify Syntactic Phrases in Free Text

<abstract><title style='display:none'>Abstract</title>Artificial Intelligence (AI) can be defined as the application of science and engineering with the intent of intelligent machine composition. It involves using tool based on intelligent behavior of humans in solving complex issues, designed in a way to make computers execute tasks that were earlier thought of human intelligence involvement. In comparison to other computational automations, AI facilitates and enables time reduction based on personnel needs and most importantly, the operational expenses.Artificial Intelligence (AI) is an area of great interest and significance in petroleum exploration and production. Over the years, it has made an impact in the industry, and the application has continued to grow within the oil and gas industry. The application in E &amp; P industry has more than 16 years of history with first application dated 1989, for well log interpretation; drill bit diagnosis using neural networks and intelligent reservoir simulator interface. It has been propounded in solving many problems in the oil and gas industry which includes, seismic pattern recognition, reservoir characterisation, permeability and porosity prediction, prediction of PVT properties, drill bits diagnosis, estimating pressure drop in pipes and wells, optimization of well production, well performance, portfolio management and general decision making operations and many more.This paper reviews and analyzes the successful application of artificial intelligence techniques as related to one of the major aspects of the oil and gas industry, drilling capturing the level of application and trend in the industry. A summary of various papers and reports associated with artificial intelligence applications and it limitations will be highlighted. This analysis is expected to contribute to further development of this technique and also determine the neglected areas in the field.</abstract>

Institute of Petroleum Engineering, Clausthal University of Technology, Germany

Mewbourne School of Petroleum and Geological Engineering, University of Oklahoma

Application Of Artificial Intelligence Methods In Drilling System Design And Operations: A Review Of The State Of The Art

<abstract>
<title style='display:none'>Abstract</title>
Equilibrium optimizer (EO) is a novel metaheuristic algorithm that exhibits superior performance in solving global optimization problems, but it may encounter drawbacks such as imbalance between exploration and exploitation capabilities, and tendency to fall into local optimization in tricky multimodal problems. In order to address these problems, this study proposes a novel ensemble algorithm called hybrid moth equilibrium optimizer (HMEO), leveraging both the moth flame optimization (MFO) and EO. The proposed approach first integrates the exploitation potential of EO and then introduces the exploration capability of MFO to help enhance global search, local fine-tuning, and an appropriate balance during the search process. To verify the performance of the proposed hybrid algorithm, the suggested HMEO is applied on 29 test functions of the CEC 2017 benchmark test suite. The test results of the developed method are compared with several well-known metaheuristics, including the basic EO, the basic MFO, and some popular EO and MFO variants. Friedman rank test is employed to measure the performance of the newly proposed algorithm statistically. Moreover, the introduced method has been applied to address the mobile robot path planning (MRPP) problem to investigate its problem-solving ability of real-world problems. The experimental results show that the reported HMEO algorithm is superior to the comparative approaches.
</abstract>

A Hybrid Equilibrium Optimizer Based on Moth Flame Optimization Algorithm to Solve Global Optimization Problems

<abstract>
<title style='display:none'>Abstract</title>
The Transformer is an important addition to the rapidly increasing list of different Artificial Neural Networks (ANNs) suited for extremely complex automation tasks. It has already gained the position of the tool of choice in automatic translation in many business solutions. In this paper, we present an automated approach to optimizing the Transformer structure based upon Simulated Annealing, an algorithm widely recognized for both its simplicity and usability in optimization tasks where the search space may be highly complex. The proposed method allows for the use of parallel computing and time-efficient optimization, thanks to modifying the structure while training the network rather than performing the two one after another. The algorithm presented does not reset the weights after changes in the transformer structure. Instead, it continues the training process to allow the results to be adapted without randomizing all the training parameters. The algorithm has shown a promising performance during experiments compared to traditional training methods without structural modifications. The solution has been released as open-source to facilitate further development and use by the machine learning community.
</abstract>

Optimizing the Structures of Transformer Neural Networks Using Parallel Simulated Annealing

<abstract>
<title style='display:none'>Abstract</title>
Atanassov’s intuitionistic fuzzy sets (IFSs) are a very convenient tool for describing alternatives/options while making decisions because they make it possible to naturally represent the pros, cons, and hesitation. The IFSs have attracted a significant interest and have been applied in various fields. Of course, their use poses some challenges. One of the main challenges is the ranking of alternatives/options described by the intuitionistic fuzzy sets, to be called for brevity the intuitionistic fuzzy alternatives. This is a crucial issue, notably for the applications, for instance, in decision making. We first present in detail and analyze the benefits of a method we introduced previously (cf. Szmidt and Kacprzyk [<xref ref-type="bibr" rid="j_jaiscr-2024-0013_ref_001">1</xref>]). For this method, we augment the original assumptions with an additional assumption, which is justified and inherently reasonable. As a result, we obtain formulas which are better justified than those previously used as they explicitly consider the arguments in favor (pro), against (con), and hesitance. Since the intuitionistic fuzzy alternatives can not be linearly ranked, then the additional assumptions during the ranking process are necessary. We address these issues and analyze examples to clarify our new approach. We examine some other methods discussed in the literature and analyze their results, and show that the new assumptions reconcile some misconceptions raised by those other papers.
</abstract>

Ranking of Alternatives Described by Atanassov’s Intuitionistic Fuzzy Sets – Reconciling Some Misunderstandings

<abstract>
<title style='display:none'>Abstract</title>
Self-supervised monocular depth estimation has been widely applied in autonomous driving and automated guided vehicles. It offers the advantages of low cost and extended effective distance compared with alternative methods. However, like automated guided vehicles, devices with limited computing resources struggle to leverage state-of-the-art large model structures. In recent years, researchers have acknowledged this issue and endeavored to reduce model size. Model lightweight techniques aim to decrease the number of parameters while maintaining satisfactory performance. In this paper, to enhance the model’s performance in lightweight scenarios, a novel approach to encompassing three key aspects is proposed: (1) utilizing LeakyReLU to involve more neurons in manifold representation; (2) employing large convolution for improved recognition of edges in lightweight models; (3) applying channel grouping and shuffling to maximize the model efficiency. Experimental results demonstrate that our proposed method achieves satisfactory outcomes on KITTI and Make3D benchmarks while having only 1.6M trainable parameters, representing a reduction of 27% compared with the previous smallest model, Lite-Mono-tiny, in monocular depth estimation.
</abstract>

Shufflemono: Rethinking Lightweight Network for Self-Supervised Monocular Depth Estimation

<abstract>
<title style='display:none'>Abstract</title>
Although object detection has achieved great success in the field of computer vision in the past few years, the performance of detecting small objects has not yet achieved ideal results. For instance, UAV aerial photography object detection plays an important role in traffic monitoring and other fields, but it faces some great challenges. The objects in aerial images are mainly small objects, the resolution of whom is low and the feature expression ability of whom is very weak. Information will be lost in high-dimensional feature maps, and this information is very important for the classification and positioning of small objects. The most common way to improve small object detection accuracy is to use high-resolution images, but this incurs additional computational costs. To address the above-mentioned problems, this article proposes a new model SINextNet, which uses a new dilated convolution module SINext block. This module is based on depth-separable convolution, and can improve the receptive field of the model. While extracting small object features, it can combine small object features with background information, greatly improving the feature expression ability of small objects. The experimental results indicate that the method proposed in this paper can achieve advanced performance across multiple aerial datasets.
</abstract>

Sinextnet: A New Small Object Detection Model for Aerial Images Based on PP-Yoloe

<abstract><title style='display:none'>Abstract</title>
Energy saving has always been a concern in production scheduling, especially in distributed hybrid flow shop scheduling problems. This study proposes a shuffled frog leaping algorithm with Q-learning (QSFLA) to solve distributed hybrid flow shop scheduling problems with energy-saving(DEHFSP) for minimizing the maximum completion time and total energy consumption simultaneously. The mathematical model is provided, and the lower bounds of two optimization objectives are given and proved. A Q-learning process is embedded in the memeplex search of QSFLA. The state of the population is calculated based on the lower bound. Sixteen search strategy combinations are designed according to the four kinds of global search and four kinds of neighborhood structure. One combination is selected to be used in the memeplex search according to the population state. An energy-saving operator is presented to reduce total energy consumption without increasing the processing time. One hundred forty instances with different scales are tested, and the computational results show that QSFLA is a very competitive algorithm for solving DEHFSP.
</abstract>

A Shuffled Frog Leaping Algorithm with Q-Learning for Distributed Hybrid Flow Shop Scheduling Problem with Energy-Saving

<abstract><title style='display:none'>Abstract</title>The adaptive educational systems within e-learning platforms are built in response to the fact that the learning process is different for each and every learner. In order to provide adaptive e-learning services and study materials that are tailor-made for adaptive learning, this type of educational approach seeks to combine the ability to comprehend and detect a person’s specific needs in the context of learning with the expertise required to use appropriate learning pedagogy and enhance the learning process. Thus, it is critical to create accurate student profiles and models based upon analysis of their affective states, knowledge level, and their individual personality traits and skills. The acquired data can then be efficiently used and exploited to develop an adaptive learning environment. Once acquired, these learner models can be used in two ways. The first is to inform the pedagogy proposed by the experts and designers of the adaptive educational system. The second is to give the system dynamic self-learning capabilities from the behaviors exhibited by the teachers and students to create the appropriate pedagogy and automatically adjust the e-learning environments to suit the pedagogies. In this respect, artificial intelligence techniques may be useful for several reasons, including their ability to develop and imitate human reasoning and decision-making processes (learning-teaching model) and minimize the sources of uncertainty to achieve an effective learning-teaching context. These learning capabilities ensure both learner and system improvement over the lifelong learning mechanism. In this paper, we present a survey of raised and related topics to the field of artificial intelligence techniques employed for adaptive educational systems within e-learning, their advantages and disadvantages, and a discussion of the importance of using those techniques to achieve more intelligent and adaptive e-learning environments.</abstract>

The Computational Intelligence Centre, School of Computer Science and Electronic Engineering University of Essex, Colchester,

United Kingdom of Great Britain and Northern Ireland

Faculty of Computing and Information Technology, King Abdulaziz University Jeddah,

A Survey of Artificial Intelligence Techniques Employed for Adaptive Educational Systems within E-Learning Platforms

<abstract><title style='display:none'>Abstract</title>The convergence and divergence are two common phenomena in swarm intelligence. To obtain good search results, the algorithm should have a balance on convergence and divergence. The premature convergence happens partially due to the solutions getting clustered together, and not diverging again. The brain storm optimization (BSO), which is a young and promising algorithm in swarm intelligence, is based on the collective behavior of human being, that is, the brainstorming process. The convergence strategy is utilized in BSO algorithm to exploit search areas may contain good solutions. The new solutions are generated by divergence strategy to explore new search areas. Premature convergence also happens in the BSO algorithm. The solutions get clustered after a few iterations, which indicate that the population diversity decreases quickly during the search. A definition of population diversity in BSO algorithm is introduced in this paper to measure the change of solutions’ distribution. The algorithm's exploration and exploitation ability can be measured based on the change of population diversity. Different kinds of partial reinitialization strategies are utilized to improve the population diversity in BSO algorithm. The experimental results show that the performance of the BSO is improved by part of solutions re-initialization strategies.</abstract>

International Doctoral Innovation Centre, The University of Nottingham Ningbo, China Division of Computer Science, University of Nottingham Ningbo, China, Ningbo, 315100, Zhejiang, China

Department of Electrical & Electronic Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, China

Department of Management Science, Shenzhen University, Shenzhen, China, Shenzhen, 518060, Guangdong, China

Division of Computer Science, University of Nottingham Ningbo, China, Ningbo, 315100, Zhejiang, China

Population Diversity Maintenance In Brain Storm Optimization Algorithm

<abstract><title style='display:none'>Abstract</title> Feature selection is the main step in classification systems, a procedure that selects a subset from original features. Feature selection is one of major challenges in text categorization. The high dimensionality of feature space increases the complexity of text categorization process, because it plays a key role in this process. This paper presents a novel feature selection method based on particle swarm optimization to improve the performance of text categorization. Particle swarm optimization inspired by social behavior of fish schooling or bird flocking. The complexity of the proposed method is very low due to application of a simple classifier. The performance of the proposed method is compared with performance of other methods on the Reuters-21578 data set. Experimental results display the superiority of the proposed method.</abstract>

Department of Computer Engineering and Information Technology, Payame Noor University (PNU)P.O.BOX 193953697, Tehran, IRAN

School of Computer Engineering Iran University of Science and Technology Tehran, IRAN

Feature Selection Using Particle Swarm Optimization in Text Categorization

<abstract><title style='display:none'>Abstract</title>Artificial Potential Filed (APF) is the most well-known method that is used in mobile robot path planning, however, the shortcoming is that the local minima. To overcome this issue, we present a deadlock free APF based path planning algorithm for mobile robot navigation. The Proposed-APF (P-APF) algorithm searches the goal point in unknown 2D environments. This method is capable of escaping from deadlock and non-reachability problems of mobile robot navigation. In this method, the effective front-face obstacle information associated with the velocity direction is used to modify the Traditional APF (T-APF) algorithm. This modification solves the deadlock problem that the T-APF algorithm often converges to local minima. The proposed algorithm is explained in details and to show the effectiveness of the proposed approach, the simulation experiments were carried out in the MATLAB environment. Furthermore, the numerical analysis of the proposed approach is given to prove a deadlock free motion of the mobile robot.</abstract>

An Artificial Potential Field Based Mobile Robot Navigation Method To Prevent From Deadlock

Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society & the University of Social Sciences in Lodz

 Print version published by: The Journal of the Polish Neural Network Society and the University of Social Sciences in Lodz  Editor-in-Chief: Prof. Leszek Rutkowski, IEEE...

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 Print version published by: The Journal of the Polish Neural Network Society and the University of Social Sciences in Lodz ...