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Machine Learning Perspective in the Convolutional Neural Network Era

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A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 43245

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Special Issue Editor

Prof. Dr. Young Im Cho

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Department of Computer Engineering, Gachon University, Seongnam-daero 1342, Republic of Korea
Interests: AI and its applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deep-learning architectures, such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks and convolutional neural networks, have found applications in fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human experts’ performance.

Neural networks were inspired by information processing and distributed communication nodes in biological systems. ANNs differ from biological brains in several aspects. Specifically, artificial neural networks tend to be static and symbolic, while the biological brain of most living organisms is dynamic and analogue.

The adjective "deep" in deep learning refers to the use of multiple layers in the network. Early work showed that a linear perceptron cannot be a universal classifier but a network with a non-polynomial activation function with one hidden layer of unbounded width can. Deep learning is a modern variation concerned with an unbounded number of layers of bounded size, which permits practical application and optimized implementation while retaining theoretical universality under mild conditions. In deep learning, the layers are also permitted to be heterogeneous and to deviate widely from biologically informed connectionist models for the sake of efficiency, trainability and understandability—hence the "structured" part.

This Special Issue focuses on a machine learning perspective in this convolutional neural network era. The following topics will be covered:

Fundamentals of machine learning algorithms;
Artificial Intelligence inference techniques;
Inference lightweight methods and models;
CNN and its application models;
Neuro-brain technologies and algorithms;
Effectiveness of Artificial Intelligence inferences;
Other related AI subjects, etc.

Prof. Dr. Young Im Cho
Guest Editor

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. Future Internet 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 1600 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

machine learning
artificial intelligence inference
inference lightweight
CNN

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Published Papers (9 papers)

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20 pages, 3661 KiB

Open AccessArticle

A Multi-Head LSTM Architecture for Bankruptcy Prediction with Time Series Accounting Data

by Mattia Pellegrino, Gianfranco Lombardo, George Adosoglou, Stefano Cagnoni, Panos M. Pardalos and Agostino Poggi

Future Internet 2024, 16(3), 79; https://doi.org/10.3390/fi16030079 - 27 Feb 2024

Cited by 1 | Viewed by 2174

Abstract

With the recent advances in machine learning (ML), several models have been successfully applied to financial and accounting data to predict the likelihood of companies’ bankruptcy. However, time series have received little attention in the literature, with a lack of studies on the application of deep learning sequence models such as Recurrent Neural Networks (RNNs) and the recent Attention-based models in general. In this research work, we investigated the application of Long Short-Term Memory (LSTM) networks to exploit time series of accounting data for bankruptcy prediction. The main contributions of our work are the following: (a) We proposed a multi-head LSTM that models each financial variable in a time window independently and compared it with a single-input LSTM and other traditional ML models. The multi-head LSTM outperformed all the other models. (b) We identified the optimal time series length for bankruptcy prediction to be equal to 4 years of accounting data. (c) We made public the dataset we used for the experiments which includes data from 8262 different public companies in the American stock market generated in the period between 1999 and 2018. Furthermore, we proved the efficacy of the multi-head LSTM model in terms of fewer false positives and the better division of the two classes. Full article

(This article belongs to the Special Issue Machine Learning Perspective in the Convolutional Neural Network Era)

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19 pages, 5975 KiB

Open AccessArticle

Autism Screening in Toddlers and Adults Using Deep Learning and Fair AI Techniques

by Ishaani Priyadarshini

Future Internet 2023, 15(9), 292; https://doi.org/10.3390/fi15090292 - 28 Aug 2023

Cited by 7 | Viewed by 2972

Abstract

Autism spectrum disorder (ASD) has been associated with conditions like depression, anxiety, epilepsy, etc., due to its impact on an individual’s educational, social, and employment. Since diagnosis is challenging and there is no cure, the goal is to maximize an individual’s ability by reducing the symptoms, and early diagnosis plays a role in improving behavior and language development. In this paper, an autism screening analysis for toddlers and adults has been performed using fair AI (feature engineering, SMOTE, optimizations, etc.) and deep learning methods. The analysis considers traditional deep learning methods like Multilayer Perceptron (MLP), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM), and also proposes two hybrid deep learning models, i.e., CNN–LSTM with Particle Swarm Optimization (PSO), and a CNN model combined with Gated Recurrent Units (GRU–CNN). The models have been validated using multiple performance metrics, and the analysis confirms that the proposed models perform better than the traditional models. Full article

(This article belongs to the Special Issue Machine Learning Perspective in the Convolutional Neural Network Era)

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17 pages, 2570 KiB

Open AccessEditor’s ChoiceArticle

Machine Learning for Data Center Optimizations: Feature Selection Using Shapley Additive exPlanation (SHAP)

by Yibrah Gebreyesus, Damian Dalton, Sebastian Nixon, Davide De Chiara and Marta Chinnici

Future Internet 2023, 15(3), 88; https://doi.org/10.3390/fi15030088 - 21 Feb 2023

Cited by 27 | Viewed by 7519

Abstract

The need for artificial intelligence (AI) and machine learning (ML) models to optimize data center (DC) operations increases as the volume of operations management data upsurges tremendously. These strategies can assist operators in better understanding their DC operations and help them make informed decisions upfront to maintain service reliability and availability. The strategies include developing models that optimize energy efficiency, identifying inefficient resource utilization and scheduling policies, and predicting outages. In addition to model hyperparameter tuning, feature subset selection (FSS) is critical for identifying relevant features for effectively modeling DC operations to provide insight into the data, optimize model performance, and reduce computational expenses. Hence, this paper introduces the Shapley Additive exPlanation (SHAP) values method, a class of additive feature attribution values for identifying relevant features that is rarely discussed in the literature. We compared its effectiveness with several commonly used, importance-based feature selection methods. The methods were tested on real DC operations data streams obtained from the ENEA CRESCO6 cluster with 20,832 cores. To demonstrate the effectiveness of SHAP compared to other methods, we selected the top ten most important features from each method, retrained the predictive models, and evaluated their performance using the MAE, RMSE, and MPAE evaluation criteria. The results presented in this paper demonstrate that the predictive models trained using features selected with the SHAP-assisted method performed well, with a lower error and a reasonable execution time compared to other methods. Full article

(This article belongs to the Special Issue Machine Learning Perspective in the Convolutional Neural Network Era)

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13 pages, 2344 KiB

Open AccessEditor’s ChoiceArticle

Forest Fire Detection and Notification Method Based on AI and IoT Approaches

by Kuldoshbay Avazov, An Eui Hyun, Alabdulwahab Abrar Sami S, Azizbek Khaitov, Akmalbek Bobomirzaevich Abdusalomov and Young Im Cho

Future Internet 2023, 15(2), 61; https://doi.org/10.3390/fi15020061 - 31 Jan 2023

Cited by 41 | Viewed by 9480

Abstract

There is a high risk of bushfire in spring and autumn, when the air is dry. Do not bring any flammable substances, such as matches or cigarettes. Cooking or wood fires are permitted only in designated areas. These are some of the regulations that are enforced when hiking or going to a vegetated forest. However, humans tend to disobey or disregard guidelines and the law. Therefore, to preemptively stop people from accidentally starting a fire, we created a technique that will allow early fire detection and classification to ensure the utmost safety of the living things in the forest. Some relevant studies on forest fire detection have been conducted in the past few years. However, there are still insufficient studies on early fire detection and notification systems for monitoring fire disasters in real time using advanced approaches. Therefore, we came up with a solution using the convergence of the Internet of Things (IoT) and You Only Look Once Version 5 (YOLOv5). The experimental results show that IoT devices were able to validate some of the falsely detected fires or undetected fires that YOLOv5 reported. This report is recorded and sent to the fire department for further verification and validation. Finally, we compared the performance of our method with those of recently reported fire detection approaches employing widely used performance matrices to test the achieved fire classification results. Full article

(This article belongs to the Special Issue Machine Learning Perspective in the Convolutional Neural Network Era)

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15 pages, 2552 KiB

Open AccessArticle

Single-Shot Global and Local Context Refinement Neural Network for Head Detection

by Jingyuan Hu and Zhouwang Yang

Future Internet 2022, 14(12), 384; https://doi.org/10.3390/fi14120384 - 19 Dec 2022

Cited by 1 | Viewed by 1817

Abstract

Head detection is a fundamental task, and it plays an important role in many head-related problems. The difficulty in creating the local and global context in the face of significant lighting, orientation, and occlusion uncertainty, among other factors, still makes this task a remarkable challenge. To tackle these problems, this paper proposes an effective detector, the Context Refinement Network (CRN), that captures not only the refined global context but also the enhanced local context. We use simplified non-local (SNL) blocks at hierarchical features, which can successfully establish long-range dependencies between heads to improve the capability of building the global context. We suggest a multi-scale dilated convolutional module for the local context surrounding heads that extracts local context from various head characteristics. In comparison to other models, our method outperforms them on the Brainwash and the HollywoodHeads datasets. Full article

(This article belongs to the Special Issue Machine Learning Perspective in the Convolutional Neural Network Era)

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14 pages, 1316 KiB

Open AccessArticle

A Machine Learning Predictive Model to Detect Water Quality and Pollution

by Xiaoting Xu, Tin Lai, Sayka Jahan, Farnaz Farid and Abubakar Bello

Future Internet 2022, 14(11), 324; https://doi.org/10.3390/fi14110324 - 8 Nov 2022

Cited by 8 | Viewed by 2728

Abstract

The increasing prevalence of marine pollution during the past few decades motivated recent research to help ease the situation. Typical water quality assessment requires continuous monitoring of water and sediments at remote locations with labour-intensive laboratory tests to determine the degree of pollution. We propose an automated water quality assessment framework where we formalise a predictive model using machine learning to infer the water quality and level of pollution using collected water and sediments samples. Firstly, due to the sparsity of sample collection locations, the amount of sediment samples of water is limited, and the dataset is incomplete. Therefore, after an extensive investigation on various data imputation methods’ performance in water and sediment datasets with different missing data rates, we chose the best imputation method to process the missing data. Afterwards, the water sediment sample will be tagged as one of four levels of pollution based on some guidelines and then the machine learning model will use a specific technique named classification to find the relationship between the data and the final result. After that, the result of prediction can be compared to the real result so that it can be checked whether the model is good and whether the prediction is accurate. Finally, the research gave improvement advice based on the result obtained from the model building part. Empirically, we show that our best model archives an accuracy of 75% after accounting for 57% of missing data. Experimentally, we show that our model would assist in automatically assessing water quality screening based on possibly incomplete real-world data. Full article

(This article belongs to the Special Issue Machine Learning Perspective in the Convolutional Neural Network Era)

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23 pages, 567 KiB

Open AccessArticle

Machine Learning for Bankruptcy Prediction in the American Stock Market: Dataset and Benchmarks

by Gianfranco Lombardo, Mattia Pellegrino, George Adosoglou, Stefano Cagnoni, Panos M. Pardalos and Agostino Poggi

Future Internet 2022, 14(8), 244; https://doi.org/10.3390/fi14080244 - 22 Aug 2022

Cited by 27 | Viewed by 6600

Abstract

Predicting corporate bankruptcy is one of the fundamental tasks in credit risk assessment. In particular, since the 2007/2008 financial crisis, it has become a priority for most financial institutions, practitioners, and academics. The recent advancements in machine learning (ML) enabled the development of several models for bankruptcy prediction. The most challenging aspect of this task is dealing with the class imbalance due to the rarity of bankruptcy events in the real economy. Furthermore, a fair comparison in the literature is difficult to make because bankruptcy datasets are not publicly available and because studies often restrict their datasets to specific economic sectors and markets and/or time periods. In this work, we investigated the design and the application of different ML models to two different tasks related to default events: (a) estimating survival probabilities over time; (b) default prediction using time-series accounting data with different lengths. The entire dataset used for the experiments has been made available to the scientific community for further research and benchmarking purposes. The dataset pertains to 8262 different public companies listed on the American stock market between 1999 and 2018. Finally, in light of the results obtained, we critically discuss the most interesting metrics as proposed benchmarks for future studies. Full article

(This article belongs to the Special Issue Machine Learning Perspective in the Convolutional Neural Network Era)

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10 pages, 335 KiB

Open AccessArticle

Polarity and Subjectivity Detection with Multitask Learning and BERT Embedding

by Ranjan Satapathy, Shweta Rajesh Pardeshi and Erik Cambria

Future Internet 2022, 14(7), 191; https://doi.org/10.3390/fi14070191 - 22 Jun 2022

Cited by 13 | Viewed by 2918

Abstract

In recent years, deep learning-based sentiment analysis has received attention mainly because of the rise of social media and e-commerce. In this paper, we showcase the fact that the polarity detection and subjectivity detection subtasks of sentiment analysis are inter-related. To this end, we propose a knowledge-sharing-based multitask learning framework. To ensure high-quality knowledge sharing between the tasks, we use the Neural Tensor Network, which consists of a bilinear tensor layer that links the two entity vectors. We show that BERT-based embedding with our MTL framework outperforms the baselines and achieves a new state-of-the-art status in multitask learning. Our framework shows that the information across datasets for related tasks can be helpful for understanding task-specific features. Full article

(This article belongs to the Special Issue Machine Learning Perspective in the Convolutional Neural Network Era)

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31 pages, 4149 KiB

Open AccessSystematic Review

Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis

by Stephen Afrifa, Tao Zhang, Peter Appiahene and Vijayakumar Varadarajan

Future Internet 2022, 14(9), 259; https://doi.org/10.3390/fi14090259 - 30 Aug 2022

Cited by 37 | Viewed by 4986

Abstract

With the effects of climate change such as increasing heat, higher rainfall, and more recurrent extreme weather events including storms and floods, a unique approach to studying the effects of climatic elements on groundwater level variations is required. These unique approaches will help people make better decisions. Researchers and stakeholders can attain these goals if they become familiar with current machine learning and mathematical model approaches to predicting groundwater level changes. However, descriptions of machine learning and mathematical model approaches for forecasting groundwater level changes are lacking. This study picked 117 papers from the Scopus scholarly database to address this knowledge gap. In a systematic review, the publications were examined using quantitative and qualitative approaches, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was chosen as the reporting format. Machine learning and mathematical model techniques have made significant contributions to predicting groundwater level changes, according to the study. However, the domain is skewed because machine learning has been more popular in recent years, with random forest (RF) methods dominating, followed by the methods of support vector machine (SVM) and artificial neural network (ANN). Machine learning ensembles have also been found to help with aspects of computational complexity, such as performance and training times. Furthermore, compared to mathematical model techniques, machine learning approaches achieve higher accuracies, according to our research. As a result, it is advised that academics employ new machine learning techniques while also considering mathematical model approaches to predicting groundwater level changes. Full article

(This article belongs to the Special Issue Machine Learning Perspective in the Convolutional Neural Network Era)

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