Efficient Artificial Intelligence Research Articles

On the effects of obfuscating speaker attributes in privacy-aware depression detection

Detection of depressive symptoms from spoken content has emerged as an efficient Artificial Intelligence (AI) tool for diagnosing this serious mental health condition. Since speech is a highly sensitive form of data, privacy-enhancing measures need to be in place for this technology to be useful. A common approach to enhance speech privacy is by using adversarial learning that involves concealing speaker’s specific attributes/identity while maintaining performance of the primary task. Although this technique works well for applications such as speech recognition, they are often ineffective for depression detection due to the interplay between certain speaker attributes and the performance of depression detection. This paper studies such interplay through a systematic study on how obfuscating specific speaker attributes (age, education) through adversarial learning impact the performance of a depression detection model. We highlight the relevance of two previously unexplored speaker attributes to depression detection, while considering a multimodal (audio-lexical) setting to highlight the relative vulnerabilities of the modalities under obfuscation. Results on a publicly available, clinically validated, depression detection dataset shows that attempts to disentangle age/education attributes through adversarial learning result in a large drop in depression detection accuracy, especially for the text modality. This calls for a revisit to how privacy mitigation should to be achieved for depression detection and any human-centric applications for that matter.

A two-stage strategy for brain-inspired unsupervised learning in spiking neural networks

Spiking Neural Networks (SNNs) are emerging as a promising model in the field of neuromorphic computing, offering possibilities for efficient bio-inspired computing. Unsupervised learning in SNNs plays a crucial role in exploring neural network dynamics and achieving efficient artificial intelligence systems. However, current unsupervised learning in SNNs mostly relies on single-stage learning strategies, which limits the possibility of combining diverse learning mechanisms and network structures, resulting in networks that lack flexibility and adaptability for low-power task processing. Inspired by the multi-stage and multi-strategy learning in the human brain, this paper proposes a two-stage learning strategy from coarse to fine to enhance the efficiency of unsupervised learning in SNNs. To quickly extract structural features from spike data in the first stage, we propose the Spiking-SOM algorithm, which effectively learns patterns in spike data and achieves clustering effects. Additionally, we integrate Rate encoding and Temporal encoding through a visual attention mechanism, where this hybrid encoding helps meet the need for rapid information processing in the first stage, while also aiding in detailed feature representation in the second stage. Compared to the traditional single-stage learning framework, this work maintains high accuracy in unsupervised classification while significantly reducing computational costs and speeding up training. This exploration offers a new direction for future learning frameworks in SNNs, helping to further improve the energy efficiency of the network.

A Temperature Sensory Leaky Integrate-and-Fire Artificial Neuron Based on Chitosan/PNIPAM Bilayer Volatile Complementary Resistive Switching Memristor.

The presence of neurons is crucial in neuromorphic computing systems as they play a vital role in modulating the strength of synapses through the release of either excitatory or inhibitory stimuli. Hence, the development of sensory neurons plays a pivotal role in broadening the scope of brain-inspired neural computing. The present study introduces an artificial sensory neuron, which is constructed using a temperature-sensitive volatile complementary resistance switch memristor based on the functional layer of the chitosan/PNIPAM bilayer. The resistive switching behavior arises from the formation and ionization of oxygen vacancy filaments, whereby the threshold voltage and low resistive resistance of the device exhibit a temperature-dependent increase within the range of 290-410 K. A functional replication of a neuron with leaky integration and firing has been successfully developed, effectively simulating essential biological functions such as firing triggered by threshold, refractory period implementation, and modulation of spiking frequency. The artificial sensory neuron exhibits characteristics similar to those of leaky integrated firing neurons that receive temperature inputs. It has the potential to control the output frequency and amplitude under varying temperature conditions, making it suitable for temperature-sensing applications. This study presents a potential hardware implementation for developing efficient artificial intelligence systems that can support temperature detections.

Game-changing intelligence: Unveiling the societal impact of artificial intelligence in game software

Efficient artificial intelligence (AI) in gaming software optimizes development, improves user experiences, and increases industrial production, all of which contribute positively to economic development and technical advancement. Possible drawbacks include employment displacement, ethical problems, algorithmic biases, and excessive reliance on AI; everyone has an impact on society’s dynamics and principles. In this study, we proposed a novel method called Fine-Tuned Ring Toss Game Optimization Adaptive Artificial Neural Network (FRTGO-AANN) to improve user experience in game software, examine social implications to promote ethical growth, explore AI transformational significance in games, and shape new game-play. In this research, we employ a dataset of 250 video game projects. The collected data are undergoing feature extraction by using a Principal Component Analysis (PCA) method. We used a few parameters for our suggested and current approaches to analyze the research’s findings. Our proposed FRTGO-AANN method achieves superior results like accuracy (96.7 %), precision (92.5 %), F1-score (95.3 %), recall (90.6 %).AI in gaming software represents a game changer, transforming user experience, narrative intricacy, along with the world of virtual reality. It has a profound social influence, altering entertainment and promoting innovation, along with difficult ethical considerations.

Efficient artificial intelligence approaches for medical image processing in healthcare: comprehensive review, taxonomy, and analysis

In healthcare, medical practitioners employ various imaging techniques such as CT, X-ray, PET, and MRI to diagnose patients, emphasizing the crucial need for early disease detection to enhance survival rates. Medical Image Analysis (MIA) has undergone a transformative shift with the integration of Artificial Intelligence (AI) techniques such as Machine Learning (ML) and Deep Learning (DL), promising advanced diagnostics and improved healthcare outcomes. Despite these advancements, a comprehensive understanding of the efficiency metrics, computational complexities, interpretability, and scalability of AI based approaches in MIA is essential for practical feasibility in real-world healthcare environments. Existing studies exploring AI applications in MIA lack a consolidated review covering the major MIA stages and specifically focused on evaluating the efficiency of AI based approaches. The absence of a structured framework limits decision-making for researchers, practitioners, and policymakers in selecting and implementing optimal AI approaches in healthcare. Furthermore, the lack of standardized evaluation metrics complicates methodology comparison, hindering the development of efficient approaches. This article addresses these challenges through a comprehensive review, taxonomy, and analysis of existing AI-based MIA approaches in healthcare. The taxonomy covers major image processing stages, classifying AI approaches for each stage based on method and further analyzing them based on image origin, objective, method, dataset, and evaluation metrics to reveal their strengths and weaknesses. Additionally, comparative analysis conducted to evaluate the efficiency of AI based MIA approaches over five publically available datasets: ISIC 2018, CVC-Clinic, 2018 DSB, DRIVE, and EM in terms of accuracy, precision, Recall, F-measure, mIoU, and specificity. The popular public datasets and evaluation metrics are briefly described and analyzed. The resulting taxonomy provides a structured framework for understanding the AI landscape in healthcare, facilitating evidence-based decision-making and guiding future research efforts toward the development of efficient and scalable AI approaches to meet current healthcare needs.

Deep learning-based voxel sampling for particle therapy treatment planning

Objective. Scanned particle therapy often requires complex treatment plans, robust optimization, as well as treatment adaptation. Plan optimization is especially complicated for heavy ions due to the variable relative biological effectiveness. We present a novel deep-learning model to select a subset of voxels in the planning process thus reducing the planning problem size for improved computational efficiency. Approach. Using only a subset of the voxels in target and organs at risk (OARs) we produced high-quality treatment plans, but heuristic selection strategies require manual input. We designed a deep-learning model based on P-Net to obtain an optimal voxel sampling without relying on patient-specific user input. A cohort of 70 head and neck patients that received carbon ion therapy was used for model training (50), validation (10) and testing (10). For training, a total of 12 500 carbon ion plans were optimized, using a highly efficient artificial intelligence (AI) infrastructure implemented into a research treatment planning platform. A custom loss function increased sampling density in underdosed regions, while aiming to reduce the total number of voxels. Main results. On the test dataset, the number of voxels in the optimization could be reduced by 84.8% (median) at <1% median loss in plan quality. When the model was trained to reduce sampling in the target only while keeping all voxels in OARs, a median reduction up to 71.6% was achieved, with 0.5% loss in the plan quality. The optimization time was reduced by a factor of 7.5 for the total AI selection model and a factor of 3.7 for the model with only target selection. Significance. The novel deep-learning voxel sampling technique achieves a significant reduction in computational time with a negligible loss in the plan quality. The reduction in optimization time can be especially useful for future real-time adaptation strategies.

Advanced method for estimating the volumetric intensity along tunnels using ANN

ABSTRACT Simulating realistic scenarios with numerical models often demands substantial computational resources, which can be excessively time-consuming. In complex Discrete Fracture Network (DFN) simulations where mutual influence among fracture parameters is crucial, efficient Artificial Intelligence (AI) algorithms offer a promising solution. This study focuses on the Monte Seco tunnel in Brazil, employing Artificial Neural Networks (ANN) with the Levenberg-Marquardt Algorithm (ANN-LM) to estimate Volumetric discontinuity intensity (P32). Comparative analysis with traditional DFN-based methods reveals superior predictive performance of the ANN model over Multiple Linear Regression (MLR). MATLAB was utilized for implementation, considering the interdependence of geometric parameters across fracture sets to estimate P32 values. Sensitivity analysis identified correlations between F1 parameters (density and trace length) and P32 estimates for F2, aiding in predicting potential tunnel instability. A Graphical User Interface (GUI) was developed to streamline calculations, replacing cumbersome spreadsheet methods.

An Efficient Artificial Intelligence (AI) and Internet of Things (IoT's) Based MEAN Stack Technology Applications

An Efficient Artificial Intelligence (AI) and Internet of Things (IoT's) Based MEAN Stack Technology Applications

An efficient artificial intelligence approach for early detection of cross-site scripting attacks

Cross-Site Scripting (XSS) attacks continue to pose a significant threat to web applications, compromising the security and integrity of user data. XSS is a web application vulnerability where malicious scripts are injected into websites, allowing attackers to execute arbitrary code in the victim’s browser. The consequences of XSS attacks can be severe, ranging from financial losses to compromising sensitive user information. XSS attacks enable attackers to deface websites, distribute malware, or launch phishing campaigns, compromising the trust and reputation of affected organizations. This study proposes an efficient artificial intelligence approach for the early detection of XSS attacks, utilizing machine learning and deep learning approaches, including Long Short-Term Memory (LSTM). Additionally, advanced feature engineering techniques, such as the Term Frequency-Inverse Document Frequency (TFIDF), are applied and compared to evaluate results. We introduce a novel approach named LSTM-TFIDF (LSTF) for feature extraction, which combines temporal and TFIDF features from the cross-site scripting dataset, resulting in a new feature set. Extensive research experiments demonstrate that the random forest method achieved a high performance of 0.99, outperforming state-of-the-art approaches using the proposed features. A k-fold cross-validation mechanism is utilized to validate the performance of applied methods, and hyperparameter tuning further enhances the performance of XSS attack detection. We have applied Explainable Artificial Intelligence (XAI) to understand the interpretability and transparency of the proposed model in detecting XSS attacks. This study makes a valuable contribution to the growing body of knowledge on XSS attacks and provides an efficient model for developers and security practitioners to enhance the security of web applications.

Various optimized machine learning techniques to predict agricultural commodity prices

AbstractRecent increases in global food demand have made this research and, therefore, the prediction of agricultural commodity prices, almost imperative. The aim of this paper is to build efficient artificial intelligence methods to effectively forecast commodity prices in light of these global events. Using three separate, well-structured models, the commodity prices of eleven major agricultural commodities that have recently caused crises around the world have been predicted. In achieving its objective, this paper proposes a novel forecasting model for agricultural commodity prices using the extreme learning machine technique optimized with the genetic algorithm. In predicting the eleven commodities, the proposed model, the extreme learning machine with the genetic algorithm, outperforms the model formed by the combination of long short-term memory with the genetic algorithm and the autoregressive integrated moving average model. Despite the fluctuations and changes in agricultural commodity prices in 2022, the extreme learning machine with the genetic algorithm model described in this study successfully predicts both qualitative and quantitative behavior in such a large number of commodities and over such a long period of time for the first time. It is expected that these predictions will provide benefits for the effective management, direction and, if necessary, restructuring of agricultural policies by providing food requirements that adapt to the dynamic structure of the countries.

Magnetization Vector Rotation Reservoir Computing Operated by Redox Mechanism.

Physical reservoir computing is a promising way to develop efficient artificial intelligence using physical devices exhibiting nonlinear dynamics. Although magnetic materials have advantages in miniaturization, the need for a magnetic field and large electric current results in high electric power consumption and a complex device structure. To resolve these issues, we propose a redox-based physical reservoir utilizing the planar Hall effect and anisotropic magnetoresistance, which are phenomena described by different nonlinear functions of the magnetization vector that do not need a magnetic field to be applied. The expressive power of this reservoir based on a compact all-solid-state redox transistor is higher than the previous physical reservoir. The normalized mean square error of the reservoir on a second-order nonlinear equation task was 1.69 × 10-3, which is lower than that of a memristor array (3.13 × 10-3) even though the number of reservoir nodes was fewer than half that of the memristor array.

Development of Efficient Models of Artificial Intelligence for Autonomous Decision Making in Dynamic Information Systems

This research paper focuses on the development of efficient artificial intelligence models for autonomous decision-making in dynamic information systems. Using innovative approaches in data analysis and algorithm optimization, we explore ways to improve model performance in dynamic environments. The results of this research can provide a deeper understanding of how artificial intelligence can operate effectively in real time, thus opening up new perspectives for application in different industries. The research includes the implementation of advanced machine learning techniques, as well as the analysis of adaptive models that can adapt to changes in the environment. Key attention is devoted to the optimization of resources in order to ensure quick and precise decision-making in dynamic situations. In addition, the work addresses the integration of the model with high-performance sensors to improve the system's ability to gather relevant information for decision-making. Through this interdisciplinary analysis, we aim to contribute to the development of intelligent systems that can autonomously react to changes and unforeseen situations in real time.

Contextually aware intelligent control agents for heterogeneous swarms

An emerging challenge in swarm shepherding research is to design effective and efficient artificial intelligence algorithms that maintain simplicity in their decision models, whilst increasing the swarm’s abilities to operate in diverse contexts. We propose a methodology to design a context-aware swarm control intelligent agent (shepherd). We first use swarm metrics to recognise the type of swarm that the shepherd interacts with, then select a suitable parameterisation from its behavioural library for that particular swarm type. The design principle of our methodology is to increase the situation awareness (i.e. contents) of the control agent without sacrificing the low computational cost necessary for efficient swarm control. We demonstrate successful shepherding in both homogeneous and heterogeneous swarms.

A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms

Deep learning has made many advances in data classification using electrocardiogram (ECG) waveforms. Over the past decade, data science research has focused on developing artificial intelligence (AI) based models that can analyze ECG waveforms to identify and classify abnormal cardiac rhythms accurately. However, the primary drawback of the current AI models is that most of these models are heavy, computationally intensive, and inefficient in terms of cost for real-time implementation. In this review, we first discuss the current state-of-the-art AI models utilized for ECG-based cardiac rhythm classification. Next, we present some of the upcoming modeling methodologies which have the potential to perform real-time implementation of AI-based heart rhythm diagnosis. These models hold significant promise in being lightweight and computationally efficient without compromising the accuracy. Contemporary models predominantly utilize 12-lead ECG for cardiac rhythm classification and cardiovascular status prediction, increasing the computational burden and making real-time implementation challenging. We also summarize research studies evaluating the potential of efficient data setups to reduce the number of ECG leads without affecting classification accuracy. Lastly, we present future perspectives on AI's utility in precision medicine by providing opportunities for accurate prediction and diagnostics of cardiovascular status in patients.

Physics-Informed Neural Networks for Quantum Control.

Quantum control is a ubiquitous research field that has enabled physicists to delve into the dynamics and features of quantum systems, delivering powerful applications for various atomic, optical, mechanical, and solid-state systems. In recent years, traditional control techniques based on optimization processes have been translated into efficient artificial intelligence algorithms. Here, we introduce a computational method for optimal quantum control problems via physics-informed neural networks (PINNs). We apply our methodology to open quantum systems by efficiently solving the state-to-state transfer problem with high probabilities, short-time evolution, and using low-energy consumption controls. Furthermore, we illustrate the flexibility of PINNs to solve the same problem under changes in physical parameters and initial conditions, showing advantages in comparison with standard control techniques.

Research on Interior Intelligent Design System Based On Image Generation Technology

This paper uses Stable Diffusion ComfyUI platform to construct a set of artificial intelligence working system in interior design field. Stable Diffusion is a deep learning image generation model that is capable of transforming text descriptions into high-quality images. Its open source nature promotes the wide application and innovative development of the technology. ComfyUI, the extension interface of Stable Diffusion, uses a node-based graphical user interface and modularization to simplify image generation. These efficient artificial intelligence image processing tools are practical in interior design, optimizing the process and improving efficiency for designers.

An efficient artificial intelligence algorithm for predicting the sensory quality of green and black teas based on the key chemical indices

The key components dominating the quality of green tea and black tea are still unclear. Here, we respectively produced green and black teas in March and June, and investigated the correlations between sensory quality and chemical compositions of dry teas by multivariate statistics, bioinformatics and artificial intelligence algorithm. The key chemical indices were screened out to establish tea sensory quality-prediction models based on the result of OPLS-DA and random forest, namely 4 flavonol glycosides of green tea and 8 indices of black tea (4 pigments, epigallocatechin, kaempferol-3-O-rhamnosyl-glucoside, ratios of caffeine/total catechins and epi/non-epi catechins). Compared with OPLS-DA and random forest, the support vector machine model had good sensory quality-prediction performance for both green tea and black tea (F1-score > 0.92), even based on the indices of fresh tea leaves. Our study explores the potential of artificial intelligence algorithm in classification and prediction of tea products with different sensory quality.

Introduction to Photonic Neural Networks

The article briefly discusses: the development of photonic neural networks of artificial intelligence, the principles of construction and stages of development of photonic neural networks. The types of training of artificial intelligence neural networks, the advantages of such networks, as well as the scope of their application for creating more efficient artificial intelligence systems based on the photonic approach are described.

Automatic Detection of Covid-19 in CT Images Using an Optimized Deep Neural Network Classifier

An exact and quick finding of Covid-19 patients plays a significant part in the initial period of medicinal treatment and prevention. Automatic recognition of COVID-19 cases using CT images may help lessen the effect of this infection on human civilization. In recent years, computer vision is the major solution for diagnosing the covid-19 disease by using CT images. Besides, many researchers had presented efficient artificial intelligence techniques for identifying Covid-19 disease. Nevertheless, the accuracy and time consumption of the model further to be improved. Thus, in this work, we propose a Covid-19 disease classification using a hybrid deep neural network with handcrafted features. The proposed approach consists of three stages namely, pre-processing, handcrafted feature extraction, and classification. Initially, the images are given to the pre-processing stage to remove the noise present in the input images. Then, we extract the hand-crafted features (Gray-Level Co-Occurrence Matrix (GLCM)) from each image. After that, the pre-processed image is given to the input of the optimized deep neural network classifier to classify an image as normal or abnormal. The proposed optimized deep neural network is a combination of a convolution neural network (CNN) and adaptive artificial jelly optimization (A2JO) algorithm. To enhance the performance of classifier accuracy, the extracted handcrafted features are fused with the hybrid deep neural network. The performance of the proposed approach was analyzed based on different metrics and performance compared with other techniques.

Experimental Demonstration of High‐Performance Physical Reservoir Computing with Nonlinear Interfered Spin Wave Multidetection

Physical reservoir computing, which is a promising method for the implementation of highly efficient artificial intelligence devices, requires a physical system with nonlinearity, fading memory, and the ability to map in high dimensions. Although it is expected that spin wave interference can perform as highly efficient reservoir computing in some micromagnetic simulations, there has been no experimental verification to date. Herein, reservoir computing is demonstrated that utilizes multidetected nonlinear spin wave interference in an yttrium‐iron‐garnet single crystal. The subject computing system achieves excellent performance when used for hand‐written digit recognition, second‐order nonlinear dynamical tasks, and nonlinear autoregressive moving average (NARMA). It is of particular note that normalized mean square errors for NARMA2 and second‐order nonlinear dynamical tasks are 1.81 × 10−2 and 8.37 × 10−5, respectively, which are the lowest figures for any experimental physical reservoir so far reported. Said high performance is achieved with higher nonlinearity and the large memory capacity of interfered spin wave multidetection.