Computational Intelligence Research Articles

Flexible Organic Electrochemical Transistors for Energy-Efficient Neuromorphic Computing.

Brain-inspired flexible neuromorphic devices are of great significance for next-generation high-efficiency wearable sensing and computing systems. In this paper, we propose a flexible organic electrochemical transistor using poly[(bithiophene)-alternate-(2,5-di(2-octyldodecyl)- 3,6-di(thienyl)-pyrrolyl pyrrolidone)] (DPPT-TT) as the organic semiconductor and poly(methyl methacrylate) (PMMA)/LiClO4 solid-state electrolyte as the gate dielectric layer. Under gate voltage modulation, an electric double layer (EDL) forms between the dielectric layer and the channel, allowing the device to operate at low voltages. Furthermore, by leveraging the double layer effect and electrochemical doping within the device, we successfully mimic various synaptic behaviors, including excitatory post-synaptic currents (EPSC), paired-pulse facilitation (PPF), high-pass filtering characteristics, transitions from short-term plasticity (STP) to long-term plasticity (LTP), and demonstrate its image recognition and storage capabilities in a 3 × 3 array. Importantly, the device's electrical performance remains stable even after bending, achieving ultra-low-power consumption of 2.08 fJ per synaptic event at -0.001 V. This research may contribute to the development of ultra-low-power neuromorphic computing, biomimetic robotics, and artificial intelligence.

Facial Frontiers: Unveiling the Potential of LBPHs and Haar Cascades in Facial Recognition for Enhanced School Security

The article aims at introducing a Facial Recognition School Security System as one solution to improve security while speeding up administrative processes in educational institutions. The need for such a system emanates from concerns over the safety of schools as well as inadequacies inherent in conventional attendance and access control methods. Inadequate methods can be manual or rely on an older technology that leads to inefficiencies, inaccuracies and breaches of security. This proposed solution exploits contemporary artificial intelligence algorithms and computer vision techniques to facilitate the reliable identification and validation of entrants thereby providing contactless approach during Pandemic times in compliance with the COVID-19 safety protocols. At this period of COVID-19 it also acts as an avenue where physical touchpoints are reduced with consideration to social distancing measures. Our method is novel in the fact that it only detects and recognizes human faces as opposed to general object detection systems. We use Local Binary Pattern Histograms (LBPH) for face recognition and Haar Cascades for face detection. The Haar Cascade algorithm employs simple rectangular features to detect faces, using a cascade of weak classifiers to achieve high detection rates. The LBPH algorithm captures local texture patterns of facial features, calculating LBP values for each pixel. Our project demonstrates variable performance across different classes, with precision ranging from 0.50 to 1.00, recall from 0.33 to 1.00, and F1 scores from 0.33 to 0.94, while achieving an overall accuracy of 0.75, indicating robust performance in certain scenarios but room for improvement in others.

The Human Brain Versus Computer: Which is Smarter?

Our thoughts, movements, memories, and decisions are all controlled by the brain, which is the hub of the human neurological system. The complexity of the human brain has increased with evolution, and many of its fascinating characteristics are still poorly understood by researchers. When it comes to mathematical computations, quantitative analysis, and game show questions, computers may be able to consistently outperform human brains, but that does not imply that computers are generally smarter. Humans are more adept at drawing conclusions about a new situation by remembering prior experiences. Qualitative analysis and emotional intelligence are skills that humans possess. The article examines a few topics pertaining to the human brain's capacity for information. The amount of information that can be stored in the brain and its capacity for retaining and reproducing text and auditory information are evaluated and estimated based on the fact that the unit of information measurement is a bit. The potentials of the computer have been examined in order to simulate the recognition of pictures that occurs in the human brain. A theory is put out that real-time modeling of these processes is challenging to implement given the capabilities of today's computers. A Comparative Study of Human and Computer Intelligence Intellect in Humans and Computers: A Comparative Analysis Human vs. Computer Intelligence: An Evaluation of Cognitive Abilities An Investigation into the Relative Intelligence of Humans and Computers Human Intelligence and Computer Intelligence: A Comparative Analysis of Human and Machine Intelligence Comparison between Humans and Computers: An Empirical Study An Examination of the Capabilities of Human and Artificial Intelligence Human and Computer Intelligence: A Comparative Study of Cognitive Functions The Great Debate: Human Intelligence vs. Artificial Intelligence

Predicting Chronic Diseases Using Nonlinear Systems

Healthcare heavily relies on advanced analytics to predict diseases and risks, with an abundance of health data being gathered through IoT and smart healthcare. Nonlinear systems and synchronization techniques play a crucial role in analyzing this data and predicting chronic diseases, such as cancer, cardiometabolic disease, and Parkinson’s disease. Using machine learning and computational intelligence, nonlinear analysis offers valuable insights into the enormous amounts of data collected in smart healthcare settings, enabling more accurate and efficient disease prediction. This chapter explores the various aspects of nonlinear systems and synchronization techniques in predictive analytics, providing a holistic view of their applications in chronic disease prediction

SAARC super smart grid: Navigating the future - unleashing the power of an energy-efficient integration of renewable energy resources in the saarc region

The major problems in the power system network of the South Asian Association for Regional Cooperation (SAARC) region include the utilization of substantial fossil fuels, which leads to significant emissions of CO2. Moreover, due to the depletion of fossil fuels, it is very difficult to manage the demand requirement of the SAARC regions, which leads to significant power shortage issues. These problems would be resolved through the integration of the substantial amount of renewable energy resources (RERs) that are available in excess in the SAARC regions into the power system networks. This will provide a futuristic roadmap to a sustainable and green energy environment in the SAARC region. In this regard, the SAARC region has devised a new plan to transform its smart grid infrastructure into the super smart grid (SSG), which would connect the SAARC countries using the high voltage transmission lines utilizing each country's substantial RERs. To accelerate this ecofriendly effort, the key objective of this review study is to evaluate the potential of the SAARC SSG for demand management and emissions reduction. This study provides a comprehensive overview of SSG technology, covering its technical background, management, power flow improvement techniques, power stability techniques, simulation models, and future steps for development in the SAARC region. First, the regional conflicts, energy deficiency, and potential of the SAARC region have been discussed. Second, a potential architecture of the SAARC SSG has been simulated. Third, a novel Markov chain modeling has been presented for demand management in the SAARC regions. The results and discussions section presents findings from simulations and models. The paper also discusses CO2 reduction techniques, including methods for capturing, storing, and utilizing carbon in biofuel. The study also discusses futuristic optimization techniques in SSG, including blockchain, federated learning, reinforcement learning, the metaverse, digital twin technology, artificial intelligence, and cloud computing. The SSG challenges and limitations section summarizes the challenges and limitations faced by SSG. Finally, a roadmap to a sustainable and green energy environment for the SAARC power grids has been identified to support the implementation of the futuristic SAARC SSG. The results exhibited that there is a good potential for successful demand management and emissions reduction through the SAARC SSG.

Innovative Applications of AIGC in Television Content Generation

Artificial Intelligence and Generative Computing (AIGC) technologies have revolutionized television content generation by enabling automated processes that were previously labor-intensive and time-consuming. This paper explores the innovative applications of AIGC in the broadcast television sector, focusing on three key areas: automated script generation, creation of virtual hosts and actors, and automated video editing. Through case studies and empirical analysis, we examine the effectiveness and impact of these technologies on content quality, production efficiency, and audience engagement. Despite significant advancements, challenges such as ethical considerations and technological limitations persist. Looking forward, the integration of AIGC promises continued advancements in transforming the landscape of television content creation.

A Systematic Review of Computational Intelligence Techniques for Channel Selection in P300-Based Brain Computer Interface Speller

Electroencephalography (EEG)-based P300 speller aids in restoring the communication and control capabilities in patients suffering from motor disabilities. However, the quality and quantity of the data collected from EEG recordings have a substantial influence on the P300 speller’s performance. Hence, selecting the optimum number of recording electrodes, i.e., channels for each user, is a significant difficulty for the P300 speller. There are two fundamental objectives of the channel selection process: (1) to extract the most crucial information from the relevant channels, hence reducing the computing complexity of P300/non-P300 signal processing operation, and (2) to lessen the potential overfitting that could result from using unwanted channels to boost performance. For obtaining the best channel subsets, different channel selection techniques, including manual, filtering, wrapper, and embedded approaches, have been applied by past researchers. This research provides an in-depth examination of recent advancements, status, challenges, and potential solutions related to channel selection strategies in P300 speller systems. Each channel selection technique is thoroughly explored, including detailed comparisons between them. The notable advantages and drawbacks of each method are emphasized along with the discussion on the future direction and scope of work in the field of channel selection in P300 speller. The review underscores that channel selection methods enable the use of a reduced number of channels without compromising classification performance. By eliminating noisy or irrelevant channels, these approaches contribute to enhanced system performance.

Brief Review for Multi-Class Brain Tumor Diseases Schemes Using Machine Learning Techniques

Brain tumor diseases have had a considerable impact worldwide, affecting millions of individuals of different age groups, including both children and adults above 20 years old. Due to they are more needed in people’s lives, using the method based classifying brain tumors by machine learning schemes has become necessary. However, healthcare applications face challenges in identifying the most suitable classification-based metric, such as accuracy, due to the utilization of recent datasets. This study paper aims to provide a thorough evaluation of computational intelligence strategies used in tumor diagnosis. Several successful data mining techniques have been implemented, including wavelet analysis and spatial pixel modulation techniques. Furthermore, feature extraction and reduction techniques, such as the Grey Level Co-occurrence Matrix (GLCM), have been used to prepare the features for classification. Magnetic resonance imaging scan (MRI) is frequently utilized for the diagnosis of brain tumor diseases which is highly applied for classification-based machine learning, The review paper was focused on gliomas, meningiomas, and pituitary adenoma diseases. Technically, the usage of kernel principal component KPCA analysis with the proposed adaptive back propagation neural network scheme produced better performance-based classification metrics, (i.e:99.84%) for the accuracy metric. The aforementioned review articles have demonstrated that usage of the machine learning-based health care applications (brain diseases) classification widely assists the patient’s outcome and operations inside the hospitals. In summary, the paper has highlighted the importance of machine learning schemes for brain tumor detection and classification, and it also provided a comprehensive analysis and comparison of the state-of-the-art to show the methods such as ;(feature extraction, feature reduction), pros, cons, and the contributions for each of them. The paper's results are considered an advantageous starting point for future works.

Enhancing the expressivity of quantum neural networks with residual connections

In noisy intermediate-scale quantum era, the research on the combination of artificial intelligence and quantum computing has been greatly developed. Here we propose a quantum circuit-based algorithm to implement quantum residual neural networks, where the residual connection channels are constructed by introducing auxiliary qubits to data-encoding and trainable blocks in quantum neural networks. We prove that when this particular network architecture is applied to a l-layer data-encoding, the number of frequency generation forms extends from one, namely the difference of the sum of generator eigenvalues, to O(l2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{{{{{\\mathcal{O}}}}}}}}({l}^{2})$$\\end{document}, and the flexibility in adjusting Fourier coefficients can also be improved. It indicates that residual encoding can achieve better spectral richness and enhance the expressivity of various parameterized quantum circuits. Extensive numerical demonstrations in regression tasks and image classification are offered. Our work lays foundation for the complete quantum implementation of classical residual neural networks and offers a quantum feature map strategy for quantum machine learning.

Computational intelligence and its dynamic development: statistical exploration, comprehensive evaluation and prospect expansion

Computational intelligence and its dynamic development: statistical exploration, comprehensive evaluation and prospect expansion

Data‐driven variational method for discrepancy modeling: Dynamics with small‐strain nonlinear elasticity and viscoelasticity

AbstractThe effective inclusion of a priori knowledge when embedding known data in physics‐based models of dynamical systems can ensure that the reconstructed model respects physical principles, while simultaneously improving the accuracy of the solution in the previously unseen regions of state space. This paper presents a physics‐constrained data‐driven discrepancy modeling method that variationally embeds known data in the modeling framework. The hierarchical structure of the method yields fine scale variational equations that facilitate the derivation of residuals which are comprised of the first‐principles theory and sensor‐based data from the dynamical system. The embedding of the sensor data via residual terms leads to discrepancy‐informed closure models that yield a method which is driven not only by boundary and initial conditions, but also by measurements that are taken at only a few observation points in the target system. Specifically, the data‐embedding term serves as residual‐based least‐squares loss function, thus retaining variational consistency. Another important relation arises from the interpretation of the stabilization tensor as a kernel function, thereby incorporating a priori knowledge of the problem and adding computational intelligence to the modeling framework. Numerical test cases show that when known data is taken into account, the data driven variational (DDV) method can correctly predict the system response in the presence of several types of discrepancies. Specifically, the damped solution and correct energy time histories are recovered by including known data in the undamped situation. Morlet wavelet analyses reveal that the surrogate problem with embedded data recovers the fundamental frequency band of the target system. The enhanced stability and accuracy of the DDV method is manifested via reconstructed displacement and velocity fields that yield time histories of strain and kinetic energies which match the target systems. The proposed DDV method also serves as a procedure for restoring eigenvalues and eigenvectors of a deficient dynamical system when known data is taken into account, as shown in the numerical test cases presented here.

Computational intelligence modelling of methylene blue adsorption by metal-organic frameworks

ABSTRACT Computational intelligence models for wastewater treatment utilise mathematical algorithms to simulate and optimise the processes involved in the removal of pollutants from wastewater, aiding in the design and operation of the wastewater treatment plant. The aim of this work was to compare the efficiency of computational models in modelling the dynamic adsorption of dyes. Metal-organic frameworks (MOFs) are considered as adsorbents because of their excellent properties. Further, the dataset used for this study was sourced from literature pertaining to the adsorption of methylene blue (MB), which is one of the commonly found dyes in wastewater. Adsorption parameters considered for the study are initial concentration, bed height, flow rate, pH and time, for the continuous adsorption process. The efficacy of the selected computational models was compared by employing various statistical metrics. Moreover, the results show the efficiency of artificial neural network and radial basis function neural network models is superior to other models based on R2 and mean square error, which were in the range of 0.95–0.999 and 10–3–10–5, respectively. In summary, the computational intelligence models serve as the best tools for the prediction of the adsorption performance of MOFs for MB.

P-201 Embryo prioritization through an AI-powered evaluation system improves the clinical outcome in IVF cycles

Abstract Study question How can the implantation rate be improved through the prioritization of embryos, particularly in the context of elective single embryo transfer (eSET)? Summary answer Based on the retrospective data of IVF treatment using 965 embryos, the AI-powered embryo-evaluation system, icONE, was developed to provide unbiased selection with promising accuracy. What is known already Over the past decade, numerous predictive models guiding IVF treatments for frozen-thawed embryo transfer (FET) success have emerged. However, most are unidimensional, focusing on factors like patient characteristics (e.g., age, reproductive hormones), embryo quality (e.g., morphokinetics), or biomarkers (e.g., genes, microRNAs). Despite efforts, many models exhibit limited AUROC (area under the receiver operating characteristic curve) values of 0.70∼0.77, suggesting a need for comprehensive consideration of maternal and embryonic factors to enhance predictive accuracy. Study design, size, duration The current study aims to develop an AI-assisted embryo prediction system. Nine hundred and sixty-five embryos from 232 patients (ages ranging from 24 to 50 years old, mean age = 36.9 years) who underwent IVF treatment during 2015–2018 were enrolled for developing an embryo prediction system. The clinical information and detail of IVF procedures of each patient and embryo were recorded and integrated to explore their impact on clinical outcomes. Participants/materials, setting, methods Pregnant outcomes were followed, including hCG examination, sac formation, and live birth. Whole genome sequencing (WGS) analysis using TE biopsy was performe. Genetic features extracted from WGS data and maternal clinicopathological characteristics were integrated and calculated by decision tree algorithm and machine learning algorithms, including Random Forest, XGBoost, LightGBM, Tabnet, etc. Main results and the role of chance To reduce the number of IVF cycles, diminish the risk associated with multiple pregnancy, and maximize the efficacy of IVF treatment, we analyzed genetic data generated from the preimplantation genetic screenings and developed a prototype of an AI-assisted program to evaluate the viability and quality of each embryo. The “intelligent cOmputing Noble Embryo” (icONE) program integrates genetic data and maternal clinical features. icONE showed classification accuracy and prediction specificity of 0.81 and 0.83, respectively, and the overall prediction accuracy of successful implantation reached to 92%. Random Forest outperformed other machine learning tools with overall positive predictive value, negative predictive value, specificity, and sensitivity of 91.8%, 91.0%, 90.3%, and 92.4%, respectively. The overall prediction accuracy of successful implantation reaches was 91.4%. Notably, the preliminary results of a randomized prospective clinical trial showed that the clinical pregnancy rate reached 77.3% (n = 81) based on the icONE’s evaluation, which was significantly higher than that of 50% (n = 85) in the non-icONE group. Patients of both advanced and young age benefited from icONE. Taken together, these results endorse the efficacy of icONE in prioritizing embryos for clinicians in IVF treatment. Limitations, reasons for caution It is noticed that our study is limited by the small sample size and all the data are collected from a single IVF center. The uniform and controlled operation of IVF cycles in a single center may cause selection bias. Wider implications of the findings Given the shift towards eSET in reproductive medicine, prioritizing embryos for implantation potential is vital in infertility treatment. The AI-powered model established in this study enhances clinical practice, optimizing parenthood chances for those facing fertility issues. Trial registration number not applicable

Innovations in Healthcare through Computational Intelligence- A Study of Smart Technologies and AI

Innovations in Healthcare through Computational Intelligence- A Study of Smart Technologies and AI

Artificial Intelligence in Accounting And Finance

The intelligence of computers or software, as opposed to the intellect of living things, mainly people, is known as artificial intelligence (AI). It is a branch of computer science that focuses on creating and researching intelligent machines. These devices could be referred to as AIs. Artificial Intelligence is widely applied in government, industry, and academia. Advanced web search engines like Google Search, recommendation systems used by YouTube, Amazon, and Netflix, human speech-based interaction like Google Assistant, Siri, and Alexa, self-driving cars like Waymo, generative and creative tools like ChatGPT and AI art, and superhuman play and analysis in strategy games like chess and Go are a few high-profile applications. In the fields of finance and accounting, artificial intelligence has had a big impact. In reality, because they save time and offer deep insights, AI-enabled finance and accounting systems are the means for businesses to remain successful competitors in a market that is becoming more and more competitive.

A critical take on the role of random and local search-oriented components of modern computational intelligence-based optimization algorithms

The concept of computational intelligence (CI)-based optimization algorithms emerged in the early 1960s as a more practical approach to the contemporary derivate-based approaches. This paved the way for many modern algorithms to arise with an unprecedented growth rate in recent years, each claiming to have a novel and present a profound breakthrough in the field. That said, many have raised concerns about the performance of these algorithms and even identified fundamental flaws that could potentially undermine the integrity of their results. On that note, the premise of this study was to replicate some of the more prevalent, fundamental components of these algorithms in an abstract format as a measure to observe their behavior in an isolated environment. Six pseudo algorithms were designed to create a spectrum of intelligence behavior ranging from absolute randomness to local search-oriented computational architecture. These were then used to solve a set of centered and non-centered benchmark suites to see if statistically different patterns would emerge. The obtained result clearly highlighted that the algorithm’s performance would suffer significantly as these benchmarks got more intricate. This is not just in terms of the number of dimensions in the search space but also the mathematical structure of the benchmark. The implication is that, in some cases, sheer processing resources can mask the algorithm’s lack of sufficient intelligence. But as importantly, this study attempted to identify some mechanics and concepts that could potentially cause or amplify this problem. For instance, the excessive use of greedy strategy, a prevalent measure embedded in many modern CI-based algorithms, has been identified as potentially one of these reasons. The result, however, highlights a more fundamental problem in the CI-based optimization field. That is, these algorithms are often treated as a black box. This perception cultivated the culture of not exploring the underlying structure of these algorithms as long as they were deemed capable of generating acceptable results, which permits similar biases to go undetected.

Intelligent Sensing, Communication, Computation, and Caching for Satellite-Ground Integrated Networks

Intelligent Sensing, Communication, Computation, and Caching for Satellite-Ground Integrated Networks

An Overview of Flood Management from a Mathematical Perspective

Floods are among the world’s most devastating natural disasters, resulting in loss of life, damage to property, and widespread infrastructure disruptions that impact millions. Flood modelling is a crucial tool for forecasting and effectively mitigating the consequences of such disasters. This study offers an overview of flood management strategies such as structural and non-structural strategies, and the current state of flood modelling, the challenges it faces, and potential future advancements. The scope of flood modelling encompasses various approaches, such as hydrological and hydraulic models, numerical simulations, rainfall-runoff analysis, remote sensing and geographic information systems (GIS), computational intelligence and robotics. The assessment delves into some of the merits and demerits of different models used to forecast the trajectory and consequences of flood events. This study explores the potential avenues for progress and innovation within the realm of flood modelling, including the integration of modern technology and multifaceted models. To enhance the control of flood hazards and reduce the societal influence of floods, the report underscores the imperative need for continuous research in the field of flood management through conventional and mathematical modelling.

RANCANG BANGUN SISTEM CHATBOT PEMESANANMENU MAKANAN DAN MINUMAN PADA RESTORAN BERBASIS TELEGRAM BOT DENGAN PENDEKATAN NATURAL LANGUAGE PROCESSING

In the era of technological advancement, especially in computer and artificial intelligence (AI), there is a significant impact on various fields, including the Food and Beverage business. The use of chatbots with AI technology enables human-computer interaction in natural language, facilitating efficient ordering, information, and customer services. Chatbots are based on Natural Language Processing (NLP) and are trained using Machine Learning techniques such as Artificial Neural Networks (ANN), with TensorFlow as the main tool. This provides ease, accuracy, and efficiency in customer service. From the conducted research, NLP- based reservation chatbots perform well and receive positive feedback from users in most aspects. Keywords: Chatbot, Artificial Intelligence (AI), Artificial Neural Network (ANN), NLP

The Future of Computing and Artificial Intelligence

The Future of Computing and Artificial Intelligence