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AI-based online proctoring: a review of the state-of-the-art techniques and open challenges
So far, this pandemic has severely affected the education sector. As education undergoes a brilliant transformation with advancing technology, the digital acquisition of knowledge has yet to find widespread use - virtual exams. Faraway Proctoring offers several advantages of using manual and primarily based technology. Although this allows students to take an exam in any field with specific technical requirements, it eliminates the need for physical research centers. It is cost-effective and easy to plan, which can be challenging to manage, especially during aggressive trials. Finally, the paper discusses the performance characteristics of different styles of web-based inspection systems, along with their limitations and challenges. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
AI-based Power Screening Solution for SARS-CoV2 Infection: A Sociodemographic Survey and COVID-19 Cough Detector
Globally, the confirmed coronavirus (SARS-CoV2) cases are being increasing day by day. Coronavirus (COVID-19) causes an acute infection in the respiratory tract that started spreading in late 2019. Huge datasets of SARS-CoV2 patients can be incorporated and analyzed by machine learning strategies for understanding the pattern of pathological spread and helps to analyze the accuracy and speed of novel therapeutic methodologies, also detect the susceptible people depends on their physiological and genetic aspects. To identify the possible cases faster and rapidly, we propose the Artificial Intelligence (AI) power screening solution for SARS- CoV2 infection that can be deployable through the mobile application. It collects the details of the travel history, symptoms, common signs, gender, age and diagnosis of the cough sound. To examine the sharpness of pathomorphological variations in respiratory tracts induced by SARS-CoV2, that compared to other respiratory illnesses to address this issue. To overcome the shortage of SARS-CoV2 datasets, we apply the transfer learning technique. Multipronged mediator for risk-averse Artificial Intelligence Architecture is induced for minimizing the false diagnosis of risk-stemming from the problem of complex dimensionality. This proposed application provides early detection and prior screening for SARS-CoV2 cases. Huge data points can be processed through AI framework that can examine the users and classify them into "Probably COVID", "Probably not COVID"and "Result indeterminate". 2021 The Authors. Published by Elsevier B.V. -
AI-based wavelet and stacked deep learning architecture for detecting coronavirus (COVID-19) from chest X-ray images
A novel coronavirus (COVID-19), belonging to a family of severe acute respiratory syndrome coronavirus 2 (SARs-CoV-2), was identified in Wuhan city, Hubei, China, in November 2019. The disease had already infected more than 681.529665 million people as of March 13, 2023. Hence, early detection and diagnosis of COVID-19 are essential. For this purpose, radiologists use medical images such as X-ray and computed tomography (CT) images for the diagnosis of COVID-19. It is very difficult for researchers to help radiologists to do automatic diagnoses by using traditional image processing methods. Therefore, a novel artificial intelligence (AI)-based deep learning model to detect COVID-19 from chest X-ray images is proposed. The proposed work uses a wavelet and stacked deep learning architecture (ResNet50, VGG19, Xception, and DarkNet19) named WavStaCovNet-19 to detect COVID-19 from chest X-ray images automatically. The proposed work has been tested on two publicly available datasets and achieved an accuracy of 94.24% and 96.10% on 4 classes and 3 classes, respectively. From the experimental results, we believe that the proposed work can surely be useful in the healthcare domain to detect COVID-19 with less time and cost, and with higher accuracy. 2023 Elsevier Ltd -
AI-Based Yolo V4 Intelligent Traffic Light Control System
With the growing number of city vehicles, traffic management is becoming a persistent challenge. Traffic bottlenecks cause significant disturbances in our everyday lives and raise stress levels, negatively impacting the environment by increasing carbon emissions. Due to the population increase, megacities are experiencing severe challenges and significant delays in their day-to-day activities related to transportation. An intelligent traffic management system is required to assess traffic density regularly and take appropriate action. Even though separate lanes are available for various vehicle types, wait times for commuters at traffic signal points are not reduced. The proposed methodology employs artificial intelligence to collect live images from signals to address this issue in the current system. This approach calculates traffic density, utilizing the image processing technique YOLOv4 for effective traffic congestion management. The YOLOv4 algorithm produces better accuracy in the detection of multiple vehicles. Intelligent monitoring technology uses a signal-switching algorithm at signal intersections to coordinate time distribution and alleviate traffic congestion, resulting in shorter vehicle waiting times. 2022 Boppuru Rudra Prathap et al., published by Sciendo. -
AI-Controlled Wind Turbine Systems: Integrating IoT and Machine Learning for Smart Grids
Advances in renewable energy technologies are pivotal in addressing the challenges posed by the depletion of traditional energy sources and their associated environmental impacts. Among these, wind energy stands out as a promising avenue, with wind turbine farms proliferating globally. However, the unpredictable nature of wind and intricate interplay between turbines necessitate innovative solutions for efficient operation and maintenance. This paper reviews advancements in intelligent control systems, notably those proposed by Smart Wind technologies. These systems leverage a network of sensors and IoT devices to gather real-time data, such as wind speed, temperature, and humidity, to optimize turbine performance. A significant focus is on turbines employing doubly-fed induction generators, which offer benefits like adjustable speed and consistent frequency operation. Their integration into smart grids introduces challenges concerning power system dynamics'security and reliability. This review delves into the dynamics, characteristics, and potential instabilities of such integrations, emphasizing the uncertainties in wind and nonlinear load predictions. A noteworthy finding is the rising prominence of artificial intelligence, particularly machine and deep learning, in predictive diagnostics. These methodologies offer costeffective, accurate, and efficient solutions, holding potential for enhancing power system stability and accuracy in the smart grid context. The Authors, published by EDP Sciences, 2024. -
AI-driven decentralized finance and the future of finance
In the evolving landscape of finance, traditional institutions grapple with challenges ranging from outdated processes to limited accessibility, hindering the industry's ability to meet the diverse needs of a modern, digital-first society. Moreover, as the world embraces Decentralized Finance (DeFi) and Artificial Intelligence (AI) technologies, there becomes a need to bridge the gap between innovation and traditional financial systems. This disconnect not only impedes progress but also limits the potential for financial inclusion and sustainable growth. AI-Driven Decentralized Finance and the Future of Finance addresses the complexities and challenges currently facing the financial industry. By exploring the transformative potential of AI in decentralized finance, this book offers a roadmap for navigating the convergence of technology and finance. From optimizing smart contracts to enhancing security and personalizing financial experiences, the book provides practical insights and real-world examples that empower professionals to leverage AI-driven strategies effectively. With a focus on regulatory challenges, ethical considerations, and emerging trends, this book teaches individuals and organizations how to harness the power of AI in finance. By fostering interdisciplinary collaboration and offering forward-thinking perspectives, the book equips readers with the knowledge and tools needed to navigate the complexities of the digital age. Through this comprehensive exploration, AI-Driven Decentralized Finance and the Future of Finance not only offers solutions to current challenges but also paves the way for a more inclusive, sustainable, and innovative future for finance. 2024 by IGI Global. All rights reserved. -
AI-driven decision-making and optimization in modern agriculture sectors
AI-driven decision-making tools have emerged as a novel technology poised to replace traditional agricultural practices. In this chapter, AI's pivotal role in steering the agricultural sector towards sustainability is highlighted, primarily through the utilization of AI techniques such as robotics, deep learning, the internet of things, image processing, and more. This chapter offers insights into the application of AI techniques in various functional areas of agriculture, including weed management, crop management, and soil management. Additionally, it underlines both the challenges and advantages presented by AIdriven applications in agriculture. In conclusion, the potential of AI in agriculture is vast, but it faces various impediments that, when properly identified and addressed, can expand its scope. This chapter serves as a valuable resource for government authorities, policymakers, and scientists seeking to explore the untapped potential of AI's significance in agriculture. 2024, IGI Global. All rights reserved. -
AI-Driven Home Climate Optimization: The Role of ChatGPT in Enhancing AC Efficiency
The convergence of Internet of Things (IoT) and Artificial Intelligence (AI) has revolutionized home automation, yet traditional air-conditioning (AC) systems still struggle with energy inefficiency. Our research presents a novel solution, integrating AI, IoT, and user-centric design with ChatGPT, to optimize AC systems responsively to occupants' needs. Our methodology employs ChatGPT's capability to analyze historical data, discern patterns, and provide intelligent recommendations for AC operation. This transcends the functions of standard smart thermostats through AI-driven decision-making, optimizing every AC operational moment for both comfort and energy conservation. The system's foundation in data-driven decisions ensures alignment with external and internal conditions, enhancing energy efficiency and user comfort. 2024 IEEE. -
AI-enabled risk identification and traffic prediction in vehicular Ad hoc Networks
The proposed research presents a two-fold approach for advancing Vehicular Ad-Hoc Networks (VANETs). Firstly, it introduces a Residual Convolutional Neural Network (RCNN) architecture to extract real-time traffic data features, enabling accurate traffic flow prediction and hazard identification. The RCNN model, trained and tested on real- world data, outperforms existing models in both accuracy and efficiency, promising improved road safety and traffic management within VANETs. Secondly, the study introduces a Genetic Algorithm-enhanced Convolutional Neural Network (GACNN) routing algorithm, challenging traditional VANET routing methods with metaheuristic techniques. Experiments in various VANET network scenarios confirm GACNN's superior performance over existing routing protocols, marking a significant step toward more efficient and adaptive VANET traffic management. 2024 Author(s). -
AI-Enhanced IoT Data Analytics for Risk Management in Banking Operations
Using IoT data analytics in conjunction with artificial intelligence (AI) has the potential to improve banking operations' risk management. Sophisticated analytical methods are necessary for the detection and management of possible risks due to the increasing complexity and amount of data generated by the banking industry. This research proposes a novel method for analysing real-time data from IoT devices by employing artificial intelligence algorithms. The risks associated with financial transactions and operations can be better and more accurately assessed using this method. Through the integration of AI's pattern recognition, anomaly detection, and predictive modelling capabilities with the massive amounts of data generated by Internet of Things devices, this project aims to substantially enhance the efficacy and efficiency of risk management approaches in the banking sector. Research like this could lead to innovative solutions that make financial institutions more resistant to rising risks by enhancing decision-making, reducing operational weaknesses, and so on. 2024 IEEE. -
AI-Powered IoT Framework for Enhancing Building Safety through Stability Detection
The rapid urbanization and increasing structural complexities of modern buildings have heightened the need for advanced monitoring systems to ensure building safety. The research presents an AI-powered IoT framework that enhances building safety through advanced stability detection mechanisms. The proposed framework employs a novel algorithm, Ensemble Learning with IoT Sensor Data Aggregation (EnIoT-SDA), which integrates ensemble learning techniques with aggregated sensor data to provide accurate and real-time stability assessments of building structures. The effectiveness of EnIoT-SDA was evaluated through a comprehensive simulation analysis, comparing its performance against existing algorithms, including Support Vector Machine (SVM), Gradient Boosting Machines (GBM), and Fuzzy Logic Systems (FLS). Simulation metrics, such as accuracy, false positive rate, computational time, and detection latency, were used to assess and compare the algorithms' performance. The results demonstrated that EnIoT-SDA outperformed the existing methods in several key areas, offering improved accuracy and reduced detection latency, thus establishing its potential as a robust solution for building safety monitoring. The study underscores the significant advancements brought by integrating ensemble learning with IoT sensor data and highlights areas for future research and development in this domain. 2024 IEEE. -
AI-powered marketing strategies in the tourism and hospitality sector
A highly competitive environment with increased demand for personalized services drives the tourism and hospitality industry to embrace immersive and intelligent technologies. Smart technologies like artificial intelligence (AI) and virtual reality (VR) assist in promotions, marketing brands, customer analysis, and ultimately leading to sustainable businesses. Marketing research is an inevitable element for any businesses that helps in understanding their customers, catering their needs, and turning them into loyal customers. Marketing strategies incorporated with smart technologies are gaining high importance in the tourism and hospitality industries due to three major outcomes such as experience enhancement, revenue improvement and effective operations. Artificial intelligence revamped the hospitality industry with customized services and tailored recommendations based on a wholesome of customer data. Virtual reality technology provides high immersive experience to boost tourism, to enhance customer experience, to influence positive travel decisions. 2024, IGI Global. All rights reserved. -
AI, mindfulness, and emotional well-being: Nurturing awareness and compassionate balance
This chapter examines the intricate relationship between artificial intelligence (AI), mindfulness, and emotional health. It explored the synergistic potential of AI and mindfulness in enhancing emotional awareness and the function of AI in promoting emotional well-being in educational, occupational, and mental health settings. The discussion addressed emerging trends and ethical considerations. It emphasized the transformative potential of AI and mindfulness in promoting emotional well-being, focusing on maintaining a compassionate balance in the AI-driven world. 2024, IGI Global. All rights reserved. -
AIFMS Autonomous Intelligent Fall Monitoring System for the Elderly Persons
Falls are the major cause of injuries and death of elders who live alone at home. Various research works have provided the best solution to the fall detection approach during the day. However, falls occur more at night due to many factors such as low or zero lighting conditions, intake of medication/drugs, frequent urination due to nocturia disease, and slippery restroom. Based on the required factors, an autonomous monitoring system based on night condition has been proposed through retro-reflective stickers pasted on their upper cloth and infrared cameras installed in the living environment of elders. The developed system uses features such as changes in orientation angle and distance between the retro-reflective stickers to identify the human shape and its characteristics for fall identification. Experimental analysis has also been performed on various events of fall and non-fall activities during the night exclusively in the living environment of the elder, and the system achieves an accuracy of 96.2% and fall detection rate of 92.9%. Copyright 2022, IGI Global. -
Aiming at digital health via mHealth application for generation Y post-pandemic scenario
Medical and health products have become a part of our lives. A health-conscious .society is the aftermath of the pandemic. The increasing role of technology has pushed people to online alternatives for medical services, progressing towards digital health. This research thus contributes to the nascent literature on the impact of mHealth apps and the consumption pattern in Bangalore in the post-pandemic scenario. This research investigates from the perspective of usage, privacy, and affordability of the mHealth apps. Results suggest that usage is positively affected by the affordability and privacy of these apps. Firstly, app developers could use the findings for different digital health marketing strategies and implementations for the mHealth app. Secondly, academics can look at other aspects such as the knowledge people possess regarding apps and their proficiency in accepting technology. Finally, the policy discussion makers can work on concerns of affordability and privacy to cater to the more significant population segment. 2023 by IGI Global. All rights reserved. -
AIoT concepts and integration: Exploring customer interaction, ethics, policy, and privacy
Integrating AIoT technologies provide businesses with increased productivity, cost savings, data-driven insights, and enhanced consumer interactions. Nevertheless, difficulties include data privacy, ethics, regulatory compliance, and technical complexities. The recommendations include transparent practices, accountability, bias mitigation, data minimization, informed consent, and ethical design. Policymakers must develop adaptable regulations, place a premium on privacy and security, and involve stakeholders. A user-centric approach and training in data ethics are essential. AIoT offers enormous potential but requires a delicate balance between innovation and responsibility, with ethics, privacy, and policy compliance at the forefront. 2024 by IGI Global. All rights reserved. -
Air Quality Index, Personality Traits and Their Impact on the Residential Satisfaction and Quality of Life: An Exploratory Path Analysis Model
The environment directly influences the behaviour, experiences, and also the well-being of people. It is not only the outside environment but the indoor environmental quality (IEQ) that also affects the well-being of its residents (Arif et al., 2016). The objective of the present study is to study the relationship between Air Quality Index (AQI), Personality traits, Residential Satisfaction, and quality of life among participants living in Bengaluru, Chennai, and Delhi NCR. A total of 685 residents aged 18-65, living in Bengaluru, Chennai, and Delhi NCR for over 2 years, who responded to the call for participation were selected for the study. Data was collected through online Google forms. Correlation and regression analysis were carried out to understand the strength and direction of the relationship between study variables. SPSS AMOS was used to estimate the measurement model and capture mediation paths. The results present an exploratory model which identifies air quality index and personality traits and their contribution towards the perceptions of residential satisfaction. The study also establishes a link between residential satisfaction and quality of life, the new ecological paradigm, and the dominant social paradigm. The present study highlights the necessity to adopt a pro-environmental approach to improve the quality of life. 2024 - IOS Press. All rights reserved. -
Air quality sensing tag /
Patent Number: 318705-001, Applicant: Divyanshu Sinha. -
Airline Twitter Sentiment Classification using Deep Learning Fusion
Since the advent of the Internet, the way people express their ideas and beliefs has undergone significant transformation. Blogs, online forums, product review websites and social media are increasingly the primary means of distributing information about new products. Twitter, in particular, is giving people a platform to air their views and opinions about a variety of events and products. In order to continually enhance the quantity and quality of their products and services, entrepreneurs constantly need input from their customers. Businesses are always looking for ways to increase the quality of their products and services. As a result, it's tough to understand the consumer's sentiments because of the large volume of data. In this research work, a Kaggle dataset of airline tweets for sentiment analysis was used. The dataset contains 11,540 reviews. We proposed an ensemble CNN, LSTM architecture for sentiment analysis. For comparison of the proposed system, LSTM alone also tested for similar dataset. LSTM was given an accuracy of 91% and the proposed ensemble framework with LSTM and CNN was given an accuracy of 93%. The experiments showed that the proposed model achieved better accuracy when compared to conventional techniques. 2022 IEEE. -
Algae-Based Nanoparticles for Contaminated Environs Nanoremediation
Currently, the rapidly growing human interference has increased the percentage of pollutants that include organic and inorganic and this has been threatening the ecosystems. Remediation by conventional physicochemical methods, bioremediation has gained immense acceptance due to their ecofriendly, economical, and sustainable approach. Microbial-based nanoparticles act as facilitators in remediating contaminants by microbial growth and immobilization of remediating agents, by inducing microbial remediating enzymes or enhanced biosurfactants that helps to improve solubility of hydrophobic hydrocarbons to create a conducive milieu for remediation. Algal-NPs can be produced easily using low-cost medium and simple scaling up process which is economically feasible. Silver nanoparticles (AgNPs) and gold nanoparticles (AuNPs) have been synthesized using Nannochloropsis sps (NN) and Chlorella vulgaris (CV), while, brown seaweeds Petalonia fascia, Colpomenia sinuosa, and Padina pavonica were used with iron oxide NPs along with their aqueous extracts. These applications have shown to be promising alternative bioremediating methods that are safe. Algal-based NPs can act as a pollution abatement device that can help to effectively target the pollutants for efficient nanobioremediation and helps to promote environmental clean-up for eliminating heavy metals, dyes, and other organic and inorganic waste from the environment. 2025 by Apple Academic Press, Inc.