The Quantum Path Kernel: A Generalized Neural Tangent Kernel for Deep Quantum Machine Learning

 Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. A key issue is how to address the inherent non-linearity of classical deep learning, a problem in the quantum domain due to the fact that the composition of an arbitrary number of quantum gates, consisting of a series of sequenti...

quantum computing Neural Tangent Kernels (NTKs) Quantum algorithms Quantum Path Kernel classical deep neural networks

An Interpretable and Accurate Deep-Learning Diagnosis Framework Modeled With Fully and Semi-Supervised Reciprocal Learning

 The deployment of automated deep-learning classifiers in clinical practice has the potential to streamline the diagnosis process and improve the diagnosis accuracy, but the acceptance of those classifiers relies on both their accuracy and interpretability. In general, accurate deep-learning classifiers provide little model interpretability, whi...

Remove noise semi-supervised learning Deep learning machine learning GlobalNet

Stock price movement prediction based on the historical data using machine learning

 predicting stock market is one of the challenging tasks in the field of computation. Physical vs. physiological elements, rational vs. illogical conduct, investor emotions, market rumors, and other factors all play a role in the prediction. All of these factors combine to make stock values very fluctuating and difficult to forecast accurately. ...

LSTM Machine learning (ML) Long Short-Term Memory (LSTM) Random Forest Regressor model Recurrent Neural Network

Self-Supervised Learning for Electroencephalography

 Decades of research have shown machine learning superiority in discovering highly nonlinear patterns embedded in electroencephalography (EEG) records compared with conventional statistical techniques. However, even the most advanced machine learning techniques require relatively large, labeled EEG repositories. EEG data collection and lab...

SSL methods EEG signals and brain function EEG pre-processing band-pass filtering SSL framework

Production-Level Artificial Intelligence Applications in Semiconductor Supply Chains

 This is a panel paper which discusses the use of Artificial Intelligence (AI) techniques to address production level problems in semiconductor manufacturing. We have gathered a group of expert semiconductor researchers and practitioners from around the world who have applied AI techniques to semiconductor problems and the paper provides their a...

NLP for supplier management AI models supply chain logistics time-series analysis AI predicts

machine learning in innovations in stroke identification

 Cerebrovascular diseases such as stroke are among the most common causes of death and disability worldwide and are preventable and treatable. Early detection of strokes and their rapid intervention play an important role in reducing the burden of disease and improving clinical outcomes. In recent years, machine learning methods have attracte...

tissue plasminogen activator ppt deep learning ppt patient’s symptoms ppt stroke prediction ppt and document Deep learning models ppt and document download

A Human-Machine Agent Based on Active Reinforcement Learning for Target Classification in Wargame

 To meet the requirements of high accuracy and low cost of target classification in modern warfare, and lay the foundation for target threat assessment, the article proposes a human-machine agent for target classification based on active reinforcement learning (TCARL_H-M), inferring when to introduce human experience guidance for model and how to au...

Active reinforcement learning adaptive learning wargaming environments machine learning algorithms and human input cutting-edge technologies

Government Scheme using Chatbot Artificial Intelligent

 In the ever-changing landscape of digital services and government initiatives, our project embarks on a mission to empower citizens with a revolutionary chatbot known as SchemeSetu. This intelligent chatbot serves as a central information hub, consolidating crucial details on governmentsponsored loans and insurance schemes from various sources....

Chatbots Government Scheme Chatbot Artificial Intelligent Artificial Intelligent

facilitation for differently abled persons various government schemes

 In the ever-changing landscape of digital services and government initiatives, our project embarks on a mission to empower citizens with a revolutionary chatbot known as SchemeSetu. This intelligent chatbot serves as a central information hub, consolidating crucial details on governmentsponsored loans and insurance schemes from various sources....

Chatbot MegaBot AI Chatbot E-Governance Chatbots Artificial Intelligent government schemes

Disease Detection in coffee plants using convolutional Neural Network

  Rust is a severe disease affecting many productive coffee regions. It is caused by pathogenic fungi that attack the underside of coffee leaves and it is characterized by the presence of yellow-orange and powdery points. If not treated, rust can cause a drop in coffee production of up to 45%. In this sense, this paper presents a contribution to th...

Disease Detection coffee plants Convolutional Neural Network

Artificial Intelligence enabled Agriculture, Food and Public Robotic stacker for mechanized Distribution Loading/Unloading of food grain

 The agriculture sector faces many challenges such as crop diseases, pest infestation, water shortage, weeds and many more. These problems lead to substantial crop loss, economic loss and also causes severe environmental hazards due to the current agriculture practices. The AI and Robotics technologies have the potential to solve these problems effi...

Survey on Quantum Circuit Compilation for Noisy Intermediate-Scale Quantum Computers - Artificial Intelligence to Heuristics

 Computationally expensive applications, including machine learning, chemical simulations, and financial modeling, are promising candidates for noisy intermediate scale quantum (NISQ) computers. In these problems, one important challenge is mapping a quantum circuit onto NISQ hardware while satisfying physical constraints of an underlying quantum ar...

Low-Dispersion Leapfrog WCS-FDTD with Artificial Anisotropy Parameters and Simulation of Hollow Dielectric Resonator Antenna Array

 An optimized three-dimensional one-step leapfrog finite-difference time-domain (FDTD) method has been investigated, which is with a weakly conditional stability (WCS) to reduce numerical dispersion further. By introducing the artificial anisotropy parameters in a cross-correspondence manner, the phase velocity error is effectively limited without a...

IFC-BD An Interpretable Fuzzy Classifier for Boosting Explainable Artificial Intelligence in Big Data

 In current Data Science applications, the course of action has derived to adapt the system behavior for the human cognition, resulting in the emerging area of explainable artificial intelligence. Among different classification paradigms, those based on fuzzy rules are suitable solutions to stress the interpretability of the global systems. However,...

Exponential Contingency Explosion Implications for Artificial General Intelligence

 The failure of complex artificial intelligence (AI) systems seems ubiquitous. To provide a model to describe these shortcomings, we define complexity in terms of a system's sensors and the number of environments or situations in which it performs. The complexity is not looked at in terms of the difficulty of design, but in the final performance of ...

Artificial Intelligence-Aided Minimum Reactive Power Control for the DAB Converter Based on Harmonic Analysis Method

 With the aim of reducing the reactive power for the dual-active-bridge (DAB) converter, this letter proposes an artificial intelligence (AI) aided minimum reactive power control scheme based on the harmonic analysis method. Specifically, as an advanced algorithm of the deep reinforcement learning (DRL), the deep deterministic policy gradient (DDPG)...

Artificial Intelligence based Control Design for Reliable Virtual Synchronous Generators

 Virtual synchronous generator (VSG) is a promising solution for inertia support of the future electricity grid to deal with the frequency stability issues caused by the high penetration of renewable generations. However, the power variation in power electronic interface converters caused by VSG emulation increases the stress on power semiconductor ...

Applications of Artificial Intelligence on the Modeling and Optimization for Analog and Mixed-Signal Circuits A Review

 Recently, there have been many studies attempting to take advantage of advancements in Artificial Intelligence (AI) in Analog and Mixed-Signal (AMS) circuit design. Automated circuit sizing optimization and improving the accuracy of performance models are the two predominant uses of AI in AMS circuit design. This paper first introduces and explains...

The Why, What and How of Artificial General Intelligence Chip Development

 The AI chips increasingly focus on implementing neural computing at low power and cost. The intelligent sensing, automation, and edge computing applications have been the market drivers for AI chips. Increasingly, the generalisation, performance, robustness, and scalability of the AI chip solutions are compared with human-like intelligence abilitie...

Edge-Cloud Collaboration Enabled Video Service Enhancement A Hybrid Human-Artificial Intelligence Scheme

  In this paper, a video service enhancement strategy is investigated under an edge-cloud collaboration framework, where video caching and delivery decisions are made in the cloud and edge respectively. We aim to guarantee the user fairness in terms of video coding rate under statistical delay constraint and edge caching capacity constraint. A hy...

Beneficial Perturbation Network for Designing General Adaptive Artificial Intelligence Systems

  The human brain is the gold standard of adaptive learning. It not only can learn and benefit from experience, but also can adapt to new situations. In contrast, deep neural networks only learn one sophisticated but fixed mapping from inputs to outputs. This limits their applicability to more dynamic situations, where the input to output mapping...

Traffic Forecasting and Decision making of investment and construction of Tourism Highway under the Background of Artificial intelligence

 Passenger traffic fore casting and decision-making of investment and construction of the tourism highway under the background of artificial intelligence is studied in this paper. As an important information asset, big data is expected to provide people with comprehensive, accurate, and real-time business insights a...