Automated chatbot web app

 One of the most important features of any online service is the quality of its customer care. However, with the development of NLP tools, businesses are considering automated chatbot solutions to keep up with the increasing demand for their products and services. In view of this, the chatbot was developed using AIML java interpreter library Progr...

chatbot Automated chatbot AI Chatbot For Website No-Code Chatbot for Your Website Chatbot Automation

Deep Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions

 Deep learning (DL), a branch of machine learning (ML), is the core technology in today’s technological advancements and innovations. Deep learning-based approaches are the state-of-the-art methods used to analyse and detect complex patterns in large datasets, such as credit card transactions. However, most credit card fraud models in the literatu...

Credit Card Fraud Detection Deep Learning for Credit Card Fraud Detection Deep Learning Artificial intelligent machine learning

Advancing Healthcare and Elderly Activity Recognition: Active Machine and Deep Learning for FineGrained Heterogeneity Activity Recognition

 This research explores the potential of technologies in human activity recognition among the elderly population. More precisely, using sensor data and implementing Active Learning (AL), Machine Learning (ML), and Deep learning (DL) techniques for elderly activity recognition. Moreover, the study leverages the HAR70+ dataset, providing insight int...

Healthcare Elderly Activity Recognition Deep Learning Heterogeneity Activity Recognition machine learning

Creating an application to identify the presence of government issued personally identifiable information (PII) embedded in documents and data, inadvertently or otherwise

 Background: In today’s digital age, a wide variety of services and processes take place online. Users of these digital facilities are required to upload government-issued containing documents or provide data for successfully availing the services. However, the uploaded documents or data which are required to facilitate these digital services and pr...

personally identifiable information government issued personally identifiable information Scanning electronic documents for personally identifiable information Protecting personally identifiable information Personally Identifiable Information Policy

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

RECOMMENDATION OF CROP AND FERTILIZER SYSTEM USING MACHINE LEARNING

 Machine learning (ML) can make use of agricultural data related to crop yield under varying Crop based nutrient levels, and climatic fluctuations to suggest appropriate crops or supplementary nutrients to achieve the highest possible production. The aim of this study was to evaluate the efficacy of five distinct ML models for a dataset sourced ...

FERTILIZER Recommendation ML models for a dataset Crop based pH temperature, rainfall, and humidity CROP AND FERTILIZER

MACHINE LEARNING FOR INTER-TURN SHORT-CIRCUIT FAULT DIAGNOSIS IN PERMANENT MAGNET SYNCHRONOUS MOTORS

 Permanent Magnet Synchronous Motor (PMSM) is widely used due to its advantages of high power density, high efficiency and so on. In order to ensure the reliability of a PMSM system, it is extremely vital to accurately diagnose the incipient faults. In this paper, a variety of optimization algorithms are utilized to realize the diagnosis of the faul...

MACHINE LEARNING INTER-TURN SHORT-CIRCUIT

THE HIDDEN SEXUAL MINORITIES: MACHINE LEARNING APPROACHES TO ESTIMATE THE SEXUAL MINORITY ORIENTATION AMONG BEIJING COLLEGE STUDENTS

 The availability of digital technology in the hands of every citizenry worldwide makes an availableunprecedented massive amount of data. The capability to process these gigantic amounts of data in real-time with Big Data Analytics (BDA) tools and Machine Learning (ML) algorithms carries many paybacks. However, the highnumber of free BDA tools, plat...

MACHINE LEARNING THE HIDDEN SEXUAL MINORITIES

MACHINE LEARNING AND MARKETING A SYSTEMATIC LITERATURE REVIEW

 Even though machine learning (ML) applications are not novel, they have gained popularity partly due to the advance in computing processing.This study explores the adoption of ML methods in marketing applications through a bibliographic review of the period 2008–2022. In this period, the adoption of ML in marketing has grown significantly. This gro...

MACHINE LEARNING A SYSTEMATIC LITERATURE

MACHINE LEARNING BASED HEALTHCARE SYSTEM FOR INVESTIGATING THE.ASSOCIATION BETWEEN DEPRESSION AND QUALITY OF LIFE

 Machine learning (ML) algorithms are nowadays widely adopted in different contexts to perform autonomous decisions and predictions. Due to the high volume of data shared in the recent years, ML algorithms are more accurate and reliable since training and testing phases are more precise. An important concept to analyze when defining ML algorithms co...

MACHINE LEARNING HEALTHCARE SYSTEM

A REVIEW ON MACHINE LEARNING STYLES IN COMPUTER VISION—TECHNIQUES AND FUTURE DIRECTIONS

 Due to the advancement in the field of Artificial Intelligence (AI), the ability to tackle entire problems of machine intelligence. Nowadays, Machine learning (ML) is becoming a hot topic due to the direct training of machines with less interaction with a human.The scenario of manual feeding of the machine is changed in the modern era, it will lear...

MACHINE LEARNING FUTURE DIRECTIONS

A MINI-REVIEW OF MACHINE LEARNING IN BIG DATA ANALYTICS APPLICATIONS, CHALLENGES, AND PROSPECTS

 The availability of digital technology in the hands of every citizenry worldwide makes an availableunprecedented massive amount of data. The capability to process these gigantic amounts of data in real-time with Big Data Analytics (BDA) tools and Machine Learning (ML) algorithms carries many paybacks. However, the highnumber of free BDA tools, plat...

MACHINE LEARNING BIG DATA ANALYTICS APPLICATIONS

UNSUPERVISED ALGORITHMS TO DETECT ZERO-DAYATTACKS STRATEGY AND APPLICATION

 ? In the last decade, researchers, practitioners and companies struggled for devising mechanisms to detect cyber-security threats. Among others, those efforts originated rule-based, signature-based or supervised Machine Learning (ML) algorithms that were proven effective for detecting those intrusions that have already been encountered and characte...

DEEP LEARNING APPLICATION

UNSUPERVISED ALGORITHMS TO DETECT ZERO-DAY.ATTACKS STRATEGY AND APPLICATION

 ? In the last decade, researchers, practitioners and companies struggled for devising mechanisms to detect cyber-security threats. Among others, those efforts originated rule-based, signature-based or supervised Machine Learning (ML) algorithms that were proven effective for detecting those intrusions that have already been encountered and characte...

DEEP LEARNING ATTACKS STRATEGY AND APPLICATION

Cyber Threat Predictive Analytics for Improving Cyber Supply Chain Security

 Cyber Supply Chain (CSC) system is complex which involves different sub-systems performing various tasks. Security in supply chain is challenging due to the inherent vulnerabilities and threats from any part of the system which can be exploited at any point within the supply chain. This can cause a severe disruption on the overall business continui...

Seizure detection using wearable sensors and machine learning: Setting a benchmark

 Tracking seizures is crucial for epilepsy monitoring and treatment evaluation. Current epilepsy care relies on caretaker seizure diaries, but clinical seizure monitoring may miss seizures. Wearable devices may be better tolerated and more suitable for long-term ambulatory monitoring. This study evaluates the seizure detection performance of custom-...

DIAGONSTIC PREDICTION AFTER COVID

 To review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at risk of being admitted to hospital for covid-19 pneumonia. Design Rapid systemat...

Energy-Efficient Secure Video Streaming in UAV-Enabled Wireless Networks A Safe-DQN Approach

 Unmanned aerial vehicles (UAVs) are anticipated to be integrated into the next generation wireless networks as new aerial mobile nodes, which can provide various live streaming applications such as surveillance, reconnaissance, etc. For such applications, due to the dynamic characteristics of traffic and wireless channels, how to guarantee the qual...

Towards Blockchain for Suitable Efficiency and Data Integrity of IoT Ecosystem Transactions

 This article analyzes the effectiveness of deploying the Blockchain technology in the implementation of the IoT ecosystem database. To this end, we assess the processing efficiency of transactions originated by smart devices and the storeddata integrity. The processing-efficiency evaluation is carried out through queue-theory-based analytical model...

Predictive Analytics for Default of Credit Card Clients

 Predictive analytics has a significant potential to support different decision processes. We aimed to compare various machine learning algorithms for the selected task, which predicts credit card clients' default based on the free available data. We chose Random Forest, AdaBoost, XGBoost, and Gradient Boosting algorithm and applied them to a prepar...

Voltage Over-scaling-based Lightweight Authentication for IoT Security

  It is a challenging task to deploy lightweight security protocols in resource-constrained IoT applications. A hardware-oriented lightweight authentication protocol based on device signature generated during voltage over-scaling (VOS) was recently proposed to address this issue. VOS-based authentication employs the computation unit such as adder...

Searching for Critical Power System Cascading Failures with Graph Convolutional Network

 Power system cascading failures become more time variant and complex because of the increasing network interconnection and higher renewable energy penetration. High computational cost is the main obstacle for a more frequent online cascading failure search, which is essential to improve system security. We propose a more efficient search framework ...

Analysis of Single Event Transients (SET) using Machine Learning (ML) and Ionizing Radiation Effects Spectroscopy (IRES)

 A methodology for automating the identification of single-event transients (SETs) through Ionizing Radiation Effects Spectroscopy (IRES) and machine learning (ML) is provided. IRES enhances the identification of SETs through statistical analysis of waveform behavior, allowing for the capture of subtle circuit dynamics changes.Automated identificati...

A Machine Learning-Assisted Model for GaN Ohmic Contacts Regarding the Fabrication Processes

 Gallium nitride (GaN) devices have been successfully commercialized due to their superior performance, especially their high-power transformation efficiency. To further reduce the power consumption of these devices, the optimization for the ohmic contacts is attracting more and more attention. In the light of the mature and powerful machine learnin...

A Generative Machine Learning-Based Approach for Inverse Design of Multilayer Metasurfaces

 The synthesis of a metasurface exhibiting a specific set of desired scattering properties is a time-consuming and resource-demanding process, which conventionally relies on many cycles of full-wave simulations.It requires an experienced designer to choose the number of the metallic layers, the scatterer shapes and dimensions, and the type and the t...

Machine Learning for Automating the Design of Millimeter-Wave Baluns

 We propose a framework to analyze mm-wave baluns directly from physical parameters by adding a dimension of Machine Learning (ML) to existing electromagnetic (EM) methods.From a generalized physical model of mm-wave baluns, we train physical-electrical Machine Learning models that both accurately and quickly compute the electrical parameters of mm-...

Multi objective Automated Type-2 Parsimonious Learning Machine to Forecast Time-Varying Stock Indices Online

 Real-time forecasting of the financial time-series data is challenging for many machine learning (ML) algorithms. First, many ML models operate offline, where they need a batch of data, which may not be available during training. Besides, due to a fixed architecture of the majority of the offline-based ML models, they suffer to deal with the uncert...

Boosted Genetic Algorithm Using Machine Learning for Traffic Control Optimization

 Traffic control optimization is a challenging task for various traffic centers around the world and the majority of existing approaches focus only on developing adaptive methods under normal (recurrent) traffic conditions. Optimizing the control plans when severe incidents occur still remains an open problem, especially when a high number of lanes ...

Deep Correlation Mining Based on Hierarchical Hybrid Networks for Heterogeneous BigData Recommendations

 The advancement of several significant technologies, such as artificial intelligence, cyber intelligence, and machine learning, has made big data penetrate not only into the industry and academic field but also our daily life along with a variety of cyber-enabled applications. In this article, we focus on a deep correlation mining method in heterog...

Electrical energy consumption prediction using machine learning for industry and smart buildings

 Machine learning (ML) methods has recently contributed very well in the advancement of the prediction models used for energy consumption. Such models highly improve the accuracy, robustness, and precision and the generalization ability of the conventional time series forecasting tools. This article reviews the state of the art of machine learning ...

Enabling Cost-Effective, SLO-Aware Machine Learning Inference Serving on Public Cloud

 The remarkable advances of Machine Learning (ML) have spurred an increasing demand for ML-as-a-Service on public cloud: developers train and publish ML models as online services to provide low-latency inference for dynamic queries. The primary challenge of ML model serving is to meet the response-time Service-Level Objectives (SLOs) of inference wo...

Edge-Cloud Computing for IoT Data Analytics: Embedding Intelligence in the Edge with Deep Learning

 Rapid growth in numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this...