Cyber Risk Assessment Framework for the Construction Industry Using Machine Learning Techniques

Buildings, Journal Year: 2024, Volume and Issue: 14(6), P. 1561 - 1561

Published: May 28, 2024

Construction 4.0 integrates digital technologies that increase vulnerability to cyber threats. A dedicated risk assessment framework is essential for proactive mitigation. However, existing studies on this subject within the construction sector are scarce, with most discussions still in preliminary stages. This study introduces a machine learning techniques, pioneering data-driven approach quantitatively assess risks while considering industry-specific vulnerabilities. The builds over 20 literature reviews related cybersecurity and semi-structured interviews two industry experts, ensuring both rigor alignment practical industrial needs. also addresses challenges of data collection proposes potential solutions, such as standardized format preset fields computers can automatically populate using from companies. Additionally, dynamic models adjust based new data, facilitating continuous monitoring tailored Furthermore, explores advanced language management, positioning them intelligent consultants provide answers security inquiries. Overall, develops conceptual aimed at creating robust, off-the-shelf management system practitioners.

Language: Английский

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Virtual Reality Technology and Artificial Intelligence for Television and Film Animation

Journal of Advanced Research in Applied Sciences and Engineering Technology, Journal Year: 2024, Volume and Issue: 43(1), P. 263 - 273

Published: April 9, 2024

Artificial intelligence technology has transformed television content and production methods resulted in the development of a new generation artificially intelligent Television. Popularising artificial improves programme content, categories, cost, efficiency. Virtual reality (VR) been widely used scientific study everyday life; thus, its use film animation (FTA) teaching researched to promote FTA learning. First, learning design uses dynamic environment modelling, real-time 3D graphic production, stereoscopic displays, sensors, other VR technologies. These four issues were due present primary method. enhances FTA's basic training teaching, course increase professional skill teaching. The application effect compares analyses classroom satisfaction, comprehensive quality evaluation, core curriculum effect. group's thorough evaluation is significantly improved, students' satisfaction with atmosphere, style, facilities 85%, 78%, 97.34%, respectively. This group can incorporate process into modelling finish work well. Compared traditional instruction, pupils are happier harvest more. Thus, instruction student engagement, efficiency, knowledge abilities. After analysing mode effects, be

Language: Английский

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Energy Consumption Monitoring and Prediction System for IT Equipment

Procedia Computer Science, Journal Year: 2024, Volume and Issue: 241, P. 272 - 279

Published: Jan. 1, 2024

This paper focuses on the monitoring and prediction of energy consumption IT equipment to make informed decisions in terms efficiency. The challenge with current systems lies their specialization, scalability integration complexities. To overcome these challenges, we propose a system for equipment. proposed solution combines an adaptable, cost-effiective energy-Efficient embedded device open source software service-oriented architecture (SOA), which offers flexibility capabilities, facilitating easy inclusion several workstation working from different environments. Several traditional Linear Regression (LR) models were evaluated using temporal window hour taking into account features. As result LR evaluation, it is established that Bayesian Ridge model was best since presented lowest error highest coefficient determination. Finally, two approaches predict consumption: Kernel Density Estimation (KDE)-based mechanism generate predictor variables order future model, KDE-based model. Numerical results show KDE measurements provides lower time response than based available dataset.

Language: Английский

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Time Orient Acceleration Gait Pattern Based FOG Prediction on Parkinson Patients Using Deep Learning and Wearable Sensors

Journal of Advanced Research in Applied Sciences and Engineering Technology, Journal Year: 2024, Volume and Issue: 47(1), P. 219 - 229

Published: June 21, 2024

The problem of predicting Freeze Gait (FoG) on Parkinson diseased patients has been well studied. There exists number approaches in FoG, which uses sensory features, EEG data and so on. However, the methods suffer to achieve higher performance. To handle this issue, an efficient Time Orient Acceleration pattern based FoG prediction model (TOAGP-FoG) is presented paper. designed attach accelerometer sensors at different ankle joints body. sensor signals are recorded gait movement long term. passed central server tracks signals. With time variant stored by model, method generates Pattern with features. At each movement, analyses patterns compute FOG Risk Support (FoGRS) towards various movement. measured according forces produced patient for stamp computes minimum force be produced. Based FoGRS value, performs prediction. proposed improves performance accuracy. Other notable aspects suggested include comparable performance, resiliency, real-time capabilities, FOG-specific integration data, advanced deep learning methodologies accurate Special Features TOAGP-FoG Multi-Sensor Configuration, Temporal Analysis, Adaptive Thresholding, Dynamic (FoGRS), Enriched Feature Extraction. offers important breakthrough predictive modelling Parkinson's disease since it integrates several features such as temporal flexibility, dynamic computation, adaptive thresholding, enriched feature extraction, multi-sensor configurations.

Language: Английский

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Development Home Automation and Safety Circuit Breaker with Esp8266 Microcontroller

Journal of Advanced Research in Applied Mechanics, Journal Year: 2024, Volume and Issue: 120(1), P. 85 - 98

Published: July 10, 2024

This study addresses common challenges in conventional home electricity usage, with a focus on safety concerns related to gas leakage. In many cases, current technology lacks immediate power usage tracking, and manual control of circuit breakers, sockets, lamps proves challenging, especially when users are away. To overcome these issues, this project employs an ESP8266 Wi-Fi Shield Arduino as microcontroller connected sensors servo motor. the proposed system, can detect leakage, lamp socket activation, manage Residual Current Circuit Breaker (RCCB) motor by utilizing Blynk apps for monitoring. The main objective is design centralized system that enables electrical appliances via smartphone. methodology involves developing program Cytron WiFi Shield, creating auto-reclosure breaker notification, building practical leakage detection prototype household applications. Additionally, lightning-induced overvoltage analyzes nuisance tripping, provides over appliance while effectively detecting hazardous leaks. approach based incorporating data from limit switch conditions, relay status, voltage sensors, consumption. results justify servo's efficient performance, reliable operations, precise sensor triggering. Despite slight variations values compared actual meter, offers successful systematic enhancing management safety.

Language: Английский

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Strategic Integration of Machine Learning for Fraud Detection in E-Commerce Transactions

Advances in electronic commerce (AEC) book series/Advances in electronic commerce series, Journal Year: 2024, Volume and Issue: unknown, P. 135 - 156

Published: Sept. 13, 2024

The rise in internet users has led to an increase online payments, but this also comes with a surge fraud. To combat this, e-commerce firms must adopt device intelligence for fraud detection. Machine learning (ML) is crucial analyzing large datasets identify suspicious patterns. This study explores the effective application of ML detecting fraudulent activities, focusing on various approaches, challenges, and recommendations. It starts overview prevalence impact fraud, highlighting need robust detection systems. Key techniques, including supervised, unsupervised, semi-supervised learning, are analyzed their strengths weaknesses. emphasizes importance continuous monitoring model adaptation evolving tactics, advocating dynamic updates feedback loops enhance By integrating algorithms effectively, businesses can improve security, safeguard revenues, build trust consumers partners.

Language: Английский

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