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AI-SECURED VISITOR MANAGEMENT SYSTEM
In general, there are so many organizations using the conventional paper log book to record the access of the visitors. This conventional method takes longer time if the number of visitors exceeds the limit. Meanwhile, security issue should be a main concern if there is an increase in the number of visi tors. This is mainly due to the operators who take a long time to verify the identification of each and every visitor when there is huge number of visi tors entering the premises. Moreover, paper log is inadequate to efficiently retrieve and archive the data after several years. This report mainly discusses about the design and implementation of Visitor Management System using Internet of Things (IoT) and Artificial Intelligence (AI). Visitor management system is a security system which is used to track visitors activities in an organization or public building. ESP32 AI Thinker is used to collect the live stream video. Face detection and identification of visitors can be achieved through Artificial Intelligence (AI). With the help of IoT, we can remote access the system for visitor monitoring. The visitors information gets stored into a cloud server which helps the user to view the visitors details on mobile phone at any time. With the help of an API, the user can control the access of a visitor into the premises through notification alerts. A website has been created in order to store the details of visitors such as photograph, arrival time with the help of dijango web application frame work and bootstrap for user interface. DigitalOcean is the cloud server which is used to store the database. To get the notifications on mobile phone, a Telegram BoT API has been created in Telegram app. This BoT also saves the arrival time and photo of the visitor. With the help of this, the user will also be notified whenever a suspicious activity occurs in the premises. This IoT networked contact less security system offers improved security. AI secured Visitor Management System is the best solution to overcome the problems existing in the conventional method as it is the easy way to identify and record the information of a visitor. 2026 by Apple Academic Press, Inc. -
AI-SECURED VISITOR MANAGEMENT SYSTEM
In general, there are so many organizations using the conventional paper log book to record the access of the visitors. This conventional method takes longer time if the number of visitors exceeds the limit. Meanwhile, security issue should be a main concern if there is an increase in the number of visi tors. This is mainly due to the operators who take a long time to verify the identification of each and every visitor when there is huge number of visi tors entering the premises. Moreover, paper log is inadequate to efficiently retrieve and archive the data after several years. This report mainly discusses about the design and implementation of Visitor Management System using Internet of Things (IoT) and Artificial Intelligence (AI). Visitor management system is a security system which is used to track visitors activities in an organization or public building. ESP32 AI Thinker is used to collect the live stream video. Face detection and identification of visitors can be achieved through Artificial Intelligence (AI). With the help of IoT, we can remote access the system for visitor monitoring. The visitors information gets stored into a cloud server which helps the user to view the visitors details on mobile phone at any time. With the help of an API, the user can control the access of a visitor into the premises through notification alerts. A website has been created in order to store the details of visitors such as photograph, arrival time with the help of dijango web application frame work and bootstrap for user interface. DigitalOcean is the cloud server which is used to store the database. To get the notifications on mobile phone, a Telegram BoT API has been created in Telegram app. This BoT also saves the arrival time and photo of the visitor. With the help of this, the user will also be notified whenever a suspicious activity occurs in the premises. This IoT networked contact less security system offers improved security. AI secured Visitor Management System is the best solution to overcome the problems existing in the conventional method as it is the easy way to identify and record the information of a visitor. 2026 by Apple Academic Press, Inc. -
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. -
AI's impact on financial services, auditing, and investment strategies: The future ahead
Artificial intelligence (AI) is poised to significantly transform the fields of finance, auditing, and investment over the coming decade. In financial services, AI is anticipated to enhance decision-making processes, improve risk management practices, and facilitate the delivery of personalized financial solutions. The auditing profession will be reshaped by AI and automation, enabling continuous auditing, real-time reporting, and more effective fraud detection mechanisms. In the investment domain, AI is expected to revolutionize portfolio management and asset allocation through advanced predictive analytics and data-driven market strategies. Despite these advancements, the rapid proliferation of AI presents considerable challenges, including ethical considerations, regulatory compliance, and the necessity of re-skilling financial professionals. This strategic overview examines both the opportunities and risks associated with AI integration and provides guidance for organizations seeking to maintain competitiveness in an increasingly AI-driven financial landscape. 2025, IGI Global Scientific Publishing. 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 Jet Erosion Behavior of FDM-Printed PLA Composites Reinforced With Steel Powder Fillers
This paper reports the air jet erosion behavior of FDM-printed polylactic acid (PLA) composites reinforced with 5 wt% and 10 wt% steel powder for solving the problem of the development of durable, sustainable, and high-performance materials for engineering applications. Test specimens were fabricated by fused deposition method with uniform dispersion of steel particles based on a twin-screw extrusion and were tested using ASTM G76 air jet erosion with angular Al2O3 particles as erodent at impact angles of 30, 60, and 90. For the material loss, pure PLA showed the maximum material loss, while steel filled composite showed significantly reduced erosion (2.38% and 14.29%, 8.16% and 18.37%, and 16.07% and 26% at 30, 60 and 90, respectively) and showed the durability of the materials and their material effective utilization. The presence of embedded steel particles was verified by SEM and confocal microscopy showing that the embedded steel particles really acted as crack stoppers, diverted the crack propagation, minimized plowing and crater formation, and improved the toughness, thus extended the potential service life and supported resource-efficient engineering solutions. Among all the compositions, the 10 wt% composite showed a better erosion resistance and the smoothest post-erosion surface owing to a higher particle density with efficiency of stress transfer. Overall, steel reinforcement significantly enhanced the erosion resistance, especially in normal impact conditions and confirmed steel-filled PLA as a suitable material for components in harsh erosive environments. 2026 The Author(s). Engineering Reports published by John Wiley & Sons Ltd. -
Air quality index improvement through machine learning and quantum computing: a framework for advancing air quality prediction using quantum-inspired metaheuristics on climate change to achieve positive health
Climate change significantly exacerbates air quality deterioration, intensifying health risks and environmental instability. Air pollution poses significant challenges to public health and environmental sustainability. Accurate prediction of the Air Quality Index (AQI) is crucial for timely interventions and policy-making. As urbanization and industrial activities intensify, there is an urgent need for accurate and real-time air quality monitoring systems. Advanced machine learning (ML) techniques have shown promise in air quality forecasting and classification. Recently, quantum-inspired computational paradigms have emerged as innovative tools to overcome the limitations of traditional models, particularly in areas like feature selection, optimization, and spatial-temporal pattern recognition. This study presents a comprehensive analysis of various machine learning and deep learning models for AQI prediction, utilizing pollutant concentration data. It also explores quantum computing-inspired approaches. We explore the efficacy of different algorithms, datasets, and preprocessing techniques. This paper critically reviews high-impact research that explores the intersection of climate-induced changes and air quality prediction using ML. It identifies trends, gaps, and emerging methodologies. We conduct a comparative analysis of datasets, prediction models, and performance metrics. The paper focuses on three case studies. The first case study focuses on the Indian aspect using an Indian dataset and the global aspects with different global datasets, and the second case study uses quantum-inspired approaches. We further evaluate the performance of 10 state-of-the-art ML models, offering a roadmap for future research and deployment. Effective air quality forecasting is vital in urban planning decisions. This also plays an essential role need in environmental management and the protection of public health. This issue directly deals with Sustainable Development Goal (SDG) 3 and SDG 13. SDG 3 is related to positive health and SDG 13 is related to climate action. Conventional predictive models in ML face challenges due to multiple reasons. Effective feature selection is one such challenge as well as effective hyperparameter tuning. These challenges limit the effectiveness of artificial intelligence models. In the proposed framework, searching is enhanced using quantum jump- and quantum mechanics-related principles. This approach leads to the development of a quantum-inspired particle swarm optimization called QPSO. QPSO is able to provide more promising results by bridging the gaps of traditional optimization techniques. Model convergence is accelerated by using quantum-inspired feature selection techniques. 2026 Elsevier Inc. 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. -
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. -
AIs Role in Semantic Segmentation for Data-Driven 3D Models of Heritage Structures
Using point cloud data from laser scanning and photogrammetry to create three-dimensional models with scan-to-BIM processes has become increasingly common in heritage conservation. During the processing of point clouds, semantically segmenting data can translate captured spatial information into intelligent data structures, enabling classified, accurate, data-driven digital models of heritage structures. Subsequently, digital models are utilized for analytical tasks like structural tests, energy optimization, etc. Artificial Intelligence (AI) has become a promising solution for automating Three-Dimensional Point Cloud Semantic Segmentation (3DPCSS), enabling a faster and more accurate composition of parametric objects within 3D modeling and management systems. However, implementing 3DPCSS solely with AI presents various technical and theoretical challenges. The geometrical complexities inherent in historical structures often necessitate manual segmentation processes or oversimplified representations that miss the unique characteristics of the building. Therefore, selecting an appropriate AI framework for 3DPCSS is essential to ensure accurate results. Multiple factors determine algorithms selection, making it challenging to categorize universal solutions. The paper highlights the key factors: 1) Data collecting tools and technologies, 2) Types of the dataset, 3) Complexity of geometrical elements, and 4) Computational tasks. AI frameworks are typically selected based on the suitability and significance of these factors relative to the projects intent. Very few studies talk about the choices of algorithms. This papers significant contribution is recognizing trends in effective data acquisition strategies through a case study in India. Additionally, it identifies state-of-the-art AI models from the past decade based on a systematic literature study. The paper infers the extensive use and advancement of hybrid approaches tailored to multi-modal data types and the specific needs of heritage projects. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Alcohol and social drinking norms as a catalyst between tourist motivation and tourist satisfaction
This article aims to understand the influence of alcohol availability, ethnicity, anti-alcohol enforcement at tourist destinations, social norms of drinking among tourists and relationships with tourist motivation and tourist satisfaction. A cross-cultural empirical study was conducted using purposive sampling of 476 tourists with partial least squaresstructural equation modelling (PLS-SEM). The results suggest that tourists perceive social norms of drinking and the availability of alcohol at destinations, when coupled with ethnicity, influence the dynamics of the relationship between tourist motivation and tourist satisfaction. The availability of alcohol at tourist destinations alone does not have any relationship either with tourist motivation or with tourist satisfaction. Based on the findings, tourist service providers and facilitators can design drinkscapes specifically based on tourists ethnic background and social norms of drinking to rejuvenate tourist destinations and make them more attractive and inclusive. As the current research considers the tourists ethnic background from a general perspective, future research may investigate the impact of subcultures to understand how ethnicity effects drinkscapes and tourist behaviours. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
Alcohol-Attributable Liver Disease in India, 20002021: Comparative Analysis Across Alcohol Policy Regimes Using GBD 2021
Alcohol use is ranked among the leading causes of liver disease in the world, and the most dreadful consequences of this condition are cirrhosis and hepatic cellular carcinoma (HCC). India has an eclectic policy environment, with bans, regulation, liberal paradigms, and the influence of such policies on the epidemiological process is inadequately studied. Based on the Global Burden of Disease (GBD) 2021 data of nine states (20002021), this study focuses on disability-adjusted life years (DALYs), years of life lost (YLLs), and years lived with disability (YLDs) due to alcohol-related cirrhosis and HCC. States were classified as prohibited (Bihar, Gujarat, Nagaland), regulated (Karnataka, Kerala, Tamil Nadu), and liberal (Goa, Punjab, Sikkim). Liberal states had the highest burdens, with Sikkim leading by approximately (410 per 100,000), followed by Goa (360 per 100,000) and Punjab (290 per 100,000), all above prohibited state averages. In Bihar, there was 27% reduction of DALY, whereas Kerala had the highest increase of 44%. More than 90% of total variation in DALYs was attributed to YLLs, with men also experiencing larger overall burden, ranging between 45 and 811 times during midlife. The panel regression displayed low cohort-level variance (R2= 0.41) but strong state-level effects (R2= 0.98), that signify a high level of heterogeneity. These results show that in addition to policies, variations in implementation, fiscal priorities, and social contexts determine the burden experienced in India, which further points to the need to implement evidence-supported, targeted interventions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Alexithymia and Internet Addiction: Mediating Role of Social Connectedness, Impulsivity, and Moderation by Depression
Internet addiction is a mounting concern in current times. Recent studies indicate a link between alexithymia and Internet addiction, but the underlying mechanisms of this association require more investigation. The present study explores the relationship between alexithymia and Internet addiction, with the mediating effect of Impulsivity and social connectedness, and the moderating effect of depression. A convenience sample of 362 participants between the ages of 18 and 25 years participated in this study and completed the Youngs Internet Addiction Test, Toronto-Alexithymia Scale, The Social Connectedness Scale, Barratt Impulsiveness Scale 15, and The Centre for Epidemiological Studies Depression Scale Revised. The results indicate that the direct effect of alexithymia on Internet addiction is partially mediated through impulsivity and social connectedness. Further, the moderating effect of depression is found to be non-significant. The results revealed two possible pathways through which alexithymia influences Internet addiction. Future research and interventions on Internet addiction can use these findings to mitigate the adverse outcomes of Internet addiction. 2025, PsychOpen. All rights reserved. -
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. -
Algorithm trading and its application in stock broking services
Purpose: Algorithmic trading provides a more systematic approach to active trading than methods based on trader intuition or instinct. The aim of the study is to examine the level of awareness among the brokers when integrated with technology for the purpose of executing the trades. Design/Methodology: A self-administered and structured 350 questionnaires were designed and circulated to collect the preliminary information from the stock brokers operating in NSE and BSE within the geographical limits of Bangalore district using the Systematic Sampling method to obtain a sample size of 235. Awareness, Automated trading, Elimination of human error, portfolio management, tracking order, order placement were the critical variables observed to validate the hypothesis using Simple Percentage Analysis & Chi-Square Analysis using Statistical Analysis Software (SAS). Findings: It was found that there is robust association between the level of awareness of the mentioned technology in its application by the stock brokers of NSE and BSE operating in Bangalore. Portfolio management and automated trading are the highly associated application of Algorithmic trading among the stock brokerage services. Originality: Algorithmic trading makes use of complex formulas, combined with mathematical models and human oversight, to make decisions to buy or sell financial securities on an exchange. It can be used in a wide variety of situations including order execution, arbitrage, and trend trading strategies. Algorithmic traders often make use of high-frequency trading technology, which can enable a firm to make tens of thousands of trades per second. The Authors, published by EDP Sciences. -
Algorithmic Crypto Trading using EMA Strategy
Algorithmic trading has transformed financial markets by enabling data-driven strategies that enhance efficiency and decision-making. This paper presents a web-based crypto currency trading platform that employs the Exponential Moving Average (EMA) strategy for automated trade execution, market trend analysis, and portfolio tracking. The platform integrates key performance metrics, including win rate, average profit per trade, risk-reward ratio, and profit factor to assess trading effectiveness. Notably, EMA-based trading achieves the highest profit factor of 3.5 which outperformed deep learning and manual trading by 9.37% and 133%, respectively. Additionally, EMA exhibits a strong win rate of 60%, compared to 65% for deep learning and 40% for manual trading, while maintaining a balanced risk-reward ratio of 2.2. The system features live data visualization, customizable watchlists, and automated trading workflows, providing traders with actionable insights with minimized human error. Performance evaluation indicates that EMA offers a superior trade-off between profitability and risk management, making it a robust and adaptable solution for navigating cryptocurrency markets. This work bridges the gap between manual trading and advanced algorithmic strategies, delivering a user-friendly and efficient trading framework. 2025 IEEE. -
Algorithmic Governance in ESG Ratings: Addressing Bias and Enhancing Transparency in AI- Driven Sustainable Finance
ESG ratings serve as guides for corporate investment and strategic decisions and are appended with numerous biases, inconsistencies, and lack of transparency. The present chapter attempts to examine the ratings of 100 companies from different industries and regions and compare S&P Global with MSCI and Sustainalytics. The chapter researches the effects of size, sector, and regional biases on scores using a mixed- method approach. Findings show that energy- intensive sectors are downgraded because of sustainability efforts; large firms get higher grades due to better disclosure, and performance is also higher for companies in jurisdictions marked by stringent regulations. Inconsistencies in ratings are illustrated further by examples of the specific study cases that deal with BlackRock and Tesla. This chapter brings an AI- enabled framework that uses blockchain and machine learning, as well as real- time information, to boost transparency and standardization in response to these issues. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Algorithmic Justice: Navigating the Ethical and Legal Landscape of AI-Powered Predictive Policing in Twenty-First Century
With the Fourth Industrial Revolution gaining traction, cyber-physical cognitive systems have thrived, fundamentally reshaping the fabric of the society we inhabit. Artificial intelligence (AI) is one such pioneering system, which thrived as a result of the intricate interplay between machine learning algorithms, deep learning (employs neural networks to facilitate automated learning) and vast repositories of structured, semi-structured or unstructured data called big data. AI was further revolutionized by development of techniques such as generative adversarial networks (GANs), transformers and large language models (LLMs) which initiated a new era of predictive analytics. One such prominent deployment of predictive data analytics has been witnessed in the sphere of law enforcement and crime controlling popularly termed as predictive policing (PP). PP is a convulsion of smart society and smart policing that uses data, algorithms and statistical modelling to foster safe, sustainable and better quality of life by optimizing resources and actions of law enforcement agencies (LEAs) to change the landscape of crime management in the society. AI-powered PP is one such breakthrough in advancement of this goal, but there are certain ethical and legal conundrum such as AI ethics, privacy, algorithmic bias and legal uncertainty that are barrier to the adoption of AI for PP. This research paper discusses the ethical and legal quandaries of AI-powered PP with further comparative analysis of key issues in two prominent democraciesUSA and India. Additionally, the paper also puts forth a way forward to achieve sustainable and harmonious use of AI for PP. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.