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Artificial Intelligence and Machine Learning in Advanced Materials Science: A New Era of Innovation
The increase of artificial intelligence (AI) and machine learning (ML) in the discovery, design, and optimization of new materials is causing a rapid acceleration of the field of materials science. This chapter addresses the principles for how artificial intelligence and machine learning can enable the predictive modeling, high-throughput screening, and smartproduction ofpolymers, alloys, ceramics, and nanomaterials. Emphasized are some of the techniques, such as hybrid approaches to artificial intelligence and physics, generative models, and reinforcement learning. Some important problems in the chapter are spoken about: lack of standards, incoherence of data, and unfeasibility of explaining the models. Along with that, this also attempts to investigate how the advantages of upcoming malware such as quantum computing, edge synthetic intelligence, and open information facilities can reinforce the research and innovation in the forthcoming age of materials. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Artificial Intelligence and Machine Learning Combined Security Enhancement Using ENIGMA
Enigma is a relatively new and emerging field that has the potential to bring significant benefits to the way contracts are executed and managed. The integration of Artificial Intelligence (AI) into smart contract technology can automate repetitive tasks, reduce the need for human intervention, improve decision-making, and provide transparency and trust. It can also provide more flexibility, handle more complex tasks, learn from past experiences, have predictive capabilities, and have human oversight and intervention. All these features make Enigma contracts more advanced than traditional smart contracts. AI-powered smart contracts, or Enigma contracts, can also improve contract execution, increase efficiency, facilitate better negotiation, and facilitate automated dispute resolution. However, as the technology is still in its early stages, major challenges and risks can adopted but the need for robust security. The potential for AI is to make decisions that are not in the best interests of its parties. Despite these challenges, the potential benefits of AI-powered smart contracts make them an area of on-going research and development that is worth exploring further. Enigma can be used or applied in various fields, and can be used to secure the sensitive information by applying robust security system. Enigma contract is a AI powered smart contract which is used to automate decision-making processes and improve its efficiency, Enigma as the name suggest it is a complex security network which has the potential to revolutionize the security system by increasing efficiency. 2023 IEEE. -
Artificial Intelligence and Machine Learning Applications in Threat Detection
Artificial intelligence (AI) and machine mastering (ML) have emerged as progressive breakthroughs in medical prognosis, especially in the area of early most cancers diagnosis. These trendy gadgets significantly enhance the accuracy and performance of tumor detection. By analyzing huge records units, synthetic intelligence structures can identify small styles and anomalies in medical imaging, which lets in malignant tumors to be recognized earlier and greater appropriately than traditional methods. AI and ML are needed to accelerate tumor analysis and category in the context of most cancers remedy. These technologies assist doctors quickly perceive any abnormalities in radiological pictures inclusive of X-rays, MRIs and CT scans. Artificial intelligence reduces the workload of clinical specialists through automating this manner, enabling faster and greater correct analysis. In addition, AI can assist tailor remedy packages contemplating a patient's specific records, maximizing treatments and predicting remedy outcomes. In addition to improving the performance and speed of diagnosis, the aggregate of AI and ML in tumor detection suggests the capability to boom the general effectiveness of cancer treatment and in the end enhance affected person consequences. 2025 Scrivener Publishing LLC. -
Artificial Intelligence and Internet of Things readiness: Inclination for hotels to support a sustainable environment
The idea of Smart Cities has been one of the key driving factors for the urban transformation to a low carbon climate, sustainable economy and mobility in recent years because of the alarming situation of global warming. One of the industries with swift growth is hotel sector and hence is one of the key contributors to carbon emission and leaves environment footprints. The new emerging concept of sustainable tourism is envisaged as an important part of the Smart Cities paradigm. Improving sustainability by saving energy is becoming a primary task toady for many hotels. A great opportunity is provided by Artificial Intelligence (AI) and Internet of Things (IoT) to assimilate different systems on a platform by encouraging and assisting hotel guests to operate through single device and optimizing hotel operations. Current research focuses to identify the strategic positions of a hotel in terms of sustainability, AI and IoT technology. Components that will be considered by Hotels for the strategic intention of adopting AI and IoT for environmental sustainability. Different development and modification needed to be taken if management wants high sustainability readiness and/or IoT readiness. This conceptual paper constructs on the comprehensive study and systematic review of different area where the hotels can feasibly implement AI and IoT for improving sustainably. 2021 Elsevier Inc. All rights reserved. -
Artificial Intelligence and Human-AI Driven Accreditation System for Higher Education Quality Assurance
This study introduces the Artificial Intelligence and Human-AI powered Accreditation System, which is meant to transform quality assurance in higher education. The suggested framework will combine human knowledge and smart automation to guarantee transparency, scalability and reliability of accreditation assessments. The data processing algorithm is based on Robust Principal Component Analysis of noise elimination and data normalization and then on the minimum-Redundancy Maximum-Relevancefeature selection algorithm. The fundamental classifier is a Graph Attention Network developed on PyTorch and PyTorch Geometric that is able to capture all the relations between institutional characteristics to make explainable judgments. A blockchain ledger is incorporated to record accreditation results to achieve security and traceability. The experimental simulations show excellent performance with high accuracy, evaluation times, and fairness than the traditional models. The system offers a strong, smart and open accreditation system that facilitates continuous improvement and accountability in the higher education system. 2025 IEEE. -
Artificial Intelligence and Human Rights: Safeguarding Data Privacy and Ethical Values in the Digital Age
In the 21st century, the rapid advancement of Artificial Intelligence (AI) presents governments with the dual challenge of fostering technological innovation while safeguarding the rights and freedoms of their citizens. This comparative analysis examines how India addresses data privacy, human rights and ethical concerns arising from AI deployment. The study begins by tracing the evolution of Indias legal and policy framework, from the Information Technology Act, 2000 to the Digital Personal Data Protection Act, 2023, assessing how these measures respond to emerging AI-related threats and their implications for individual privacy and civil liberties. Further, the paper evaluates Indias engagement with international standards and best practices. Lastly, the study examines ethical and human rights challenges posedby AIapplications, including algorithmic bias, automateddecisionmaking, surveillance and deepfakes with a focus on high-stakes sectors like law enforcement, and welfare delivery. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development. -
Artificial intelligence and deep learning based driverless cars to reduce the road accident, death rate using python /
Patent Number: 202221047470, Applicant: Rashel Sarkar.
2% of global deaths each year are caused by automobile accidents. This corresponds to around 3,287 each day, or 1,300,000 per year. 20 million to 50 million people are seriously injured in automobile accidents annually. Why do these recurring problems persist People do make errors. One careless or foolish action is all it takes to transform a safe drive into one that could kill someone. This holds true regardless of whether the driver is preoccupied, intoxicated, or simply careless or irresponsible. In terms of technology, Artificial Intelligence (AI) has always been ahead of the curve. -
Artificial intelligence and deep learning based driverless cars to reduce the road accident, death rate using python /
Patent Number: 202221047470, Applicant: Rashel Sarkar.
2% of global deaths each year are caused by automobile accidents. This corresponds to around 3,287 each day, or 1,300,000 per year. 20 million to 50 million people are seriously injured in automobile accidents annually. Why do these recurring problems persist People do make errors. One careless or foolish action is all it takes to transform a safe drive into one that could kill someone. This holds true regardless of whether the driver is preoccupied, intoxicated, or simply careless or irresponsible. In terms of technology, Artificial Intelligence (AI) has always been ahead of the curve. -
Artificial Intelligence and Deep Learning Based Brain Tumor Detection Using Image Processing
In the field of medical science, applications that are particularly used for diagnostic purposes, are used in the detection of brain tumors since detecting an error in MRI scanning is becoming a major task for radiologists and requires a lot of their focus. Flaws that are prevalent during tumor detection must be taken care of to avoid further complications. MRI scanning is one of the most recently developing technologies. The radiologist is a key player in the identification of the brain tumor. Radiologists have to check every image perfectly to avoid the errors in identifying the brain tumor. There is a probability that sometimes cerebral fluid may also appear as mass tissue during the MRI scan. The model that is proposed in this research uses a machine learning algorithm which helps to improve the validity of the classification of the images that are taken in MRI scans. The study focuses on having an automated system that carries out an essential role in determining whether a lump is present in the brain or not. The study tries to resolve the primary flaws in detection necessary to evade further complications in MRI images in brain detection. The main aim of this study is to train the algorithm in a more extensive dataset and to check the patient-level validity with the help of various new datasets. 2023 IEEE. -
Artificial Intelligence & Data Warehouse Regional Human Resource Management Decision Support System
High-quality data is utilized to make informed decisions that effectively help to successfully safeguard our environment. When there is an abundance of information that is both heterogeneous in nature (coming from a wide variety of fields or sources) and of unknown quality, various problems may occur. Furthermore, the problem's dynamic nature also imposes some other complications. In order to deal with such complications, the central role played by supercomputers in the modern environment is to promote protection initiatives like monitoring, data analysis, communication, and information storage and retrieval. In current days, the higher dependency on the data management process forced the developers to integrate and enhance all these initiatives with Artificial Intelligence knowledge-based techniques so that smart systems can be utilized by a vast number of people. In this context, this study has illustrated how Artificial Intelligence methods have changed the nature of Environmental Decision Support Systems (EDSS) over the course of the last two decades. The strengths that an EDSS should exhibit have been emphasized in this review. In the final section, we look at some of the more innovative solutions used for various environmental issues. 2022 IEEE. -
Artificial Intelligence & Automation: Opportunities and Challenges
Artificial Intelligence (AI) and Automation innovation are growing at a steady rate that are changing organizations and bringing efficiency and adding to the economic development. The utilization of AI and robotization will likewise help improve different areas from wellbeing to horticulture. Furthermore, utilizing Automation and Artificial Intelligence would, follow the schedule, transform the idea of work and the working environment itself. For sure, machines will actually do large numbers of the undertakings typically done by people, just as supplement manual work and play out certain errands that an individual wouldn't have the option to do. Consequently, AI and mechanization have a great deal to bring to organizations and enterprises worldwide. This research paper comes up with a rundown through the blooming of Artificial Intelligence and Automation. We explored the existing potentiality of cognitive emerging technologies. This paper outlines the discussion about artificial intelligence and automation technologies and an overview of the applications. 2023 American Institute of Physics Inc.. All rights reserved. -
Artificial Intelligence (AI) in CRM (Customer Relationship Management): A Sentiment Analysis Approach
The use of customer relationship management (CRM) in marketing is examined in this essay. It looks at how CRM makes it possible to use reviews, integrate AI, conduct marketing in real time, and conduct more regular marketing operations. CRM tactics are illustrated through case studies of businesses like Uber, T-Mobile, Amazon, Apple, and Apple. CRM offers centralized data, better marketing and sales, and better customer support. There is also a discussion of the ethical, private, security, adoption, and scalability challenges of AI in CRM. In general, CRM makes data-driven decisions and customer insights easier to achieve to increase growth, loyalty, and engagement. 2024 IEEE. -
Artificial intelligence (AI) governance in organizational decision-making: balancing autonomy, accountability and transparency
Purpose This study aims to investigate the ethical and practical implications of delegating decision-making to AI systems, focusing on the necessity for a robust governance framework. Specifically, it examines how autonomy, transparency and accountability within AI governance influence organizational decision-making. Design/methodology/approach Employing a quantitative survey methodology, this study gathered data from 452 business owners and managers in Indian IT companies. The questionnaire was disseminated using online platforms and departmental communication channels. Structural equation modelling (SEM) was utilized for data analysis, allowing for the examination of relationships among autonomy, transparency, accountability and decision-making. Findings The findings indicate that autonomy, transparency and accountability significantly impact organizational decision-making processes. Specifically, autonomy and accountability were found to directly influence decision-making, while transparency also played a crucial role. Additionally, social innovation was identified as a significant moderating factor, enhancing the relationship between AI governance and decision-making outcomes. Originality/value This research contributes to the existing literature on AI governance by elucidating the critical role of ethical frameworks in organizational decision-making. By incorporating social innovation as a moderating variable, the study offers novel insights into how AI governance can be optimized to enhance decision-making processes. The application of SEM provides a rigorous analytical approach, facilitating a deeper understanding of the interplay between governance dimensions and decision-making outcomes. The findings have practical implications for organizations seeking to implement effective AI governance strategies in their operations. 2025 Emerald Publishing Limited -
Artificial Intelligence (AI) for IT Energy Efficiency and Green AI for Environment Sustainability
This volume approaches Artificial Intelligence for sustainability from two angles. First, it looks at AI systems themselves, which consume a surprising amount of energy to function, and examines ways they can be made more efficient and sustainable. Secondly, it examines how AI can be used to create efficiencies and make buildings, power grids, the manufacturing sector, and more sustainable. The chapters here also provide a comprehensive but compact overview of the latest in AI technologies and how they work. This volume will be useful to AI engineers, data scientists, software developers, academics, researchers, and more. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Artificial Intelligence - Based Steganography Model for Social Media Data Set
Steganography, one of the data security mechanisms under our investigation, shields legitimate messages from hackers and spies by employing data hiding. Data protection is newlinecurrently the top priority due to the signifcant advancements in information technology due to high-security concerns. Traditional techniques for maintaining data confdentiality include steganography and cryptography; the distinction is that steganography does not naturally arouse suspicion, whereas cryptography does. Traditional linguistic steganographic methods suffer from limitations in automation, accuracy, and the volume of concealed text. The robustness and undetectability properties of these approaches also require improvement. Third-party vulnerability is often too high for conventional techniques to handle. Artifcial intelligence is increasingly replacing traditional model creation in steganography. Despite the fact that steganography ensures security, information sent over online social networks (OSN) is plainly not safe. Steganography along newlinewith encryption can make a difference with regard to privacy of information in transit. newlineThe research study aims to build algorithms or models and assess steganography s robustness, security, undetectability, and embedding ability. Two distinct types of data newlineconcealing employed for investigation: text and image. The results were encouraging newlinewhen we initially tested our Laplacian model using image steganography and compared newlinewith benchmark methods. The second experiment, which is based on AI, generates the cover text using secret information, examines the security and robustness of steganography. The study compared suggested text steganography model, 3-bit data concealing, with other existing techniques in order to ascertain the undetectability factor. The frst experiment used MATLAB tools, and the second used the markovify python module, RNN (Recurrent Neural networks), and the Huffman tree. Further format-based steganography methods utilized in the following experiment. -
Artificial intelligence talent acquisition in HEIs recruitments
Purpose: The current research study aims to examine the application feasibility and impact of artificial intelligence (AI) among higher educational institutions (HEIs) in talent acquisitions (TA). Design/methodology/approach: A systematic sampling method was adopted to collect the responses from the 385 staff working across the various levels of management in HEIs in metropolitan cities in India. JAMOVI & SmartPLS 4 were applied to validate the hypothesis by performing the simple percentage analysis and structural equation modelling. The demographic and construct variables considered were adoption, actual usage, perceived usefulness, perceived ease of use and talent management. Findings: The key indicators of perceived usefulness are productivity, perceived ease of use, adaptability, candidate experience with the adoption of AI, frequency in decision-making in its actual usage and career path of development in the HEIs. These are the most influential items impacting the application of AI in TA. Originality/value: AI has the potential to revolutionize TA in HEIs in the form of enhanced efficiency, improved candidate experience, more objective hiring decisions, talent analytics and risk automation. However, they facilitate resume screening, candidate sourcing, applicant tracking, interviewing and predictive analytics for attrition. 2024, Emerald Publishing Limited. -
Artificial immune system based frameworks and its application in cyber immune system: A comprehensive review
Computer science has always mixed the concepts of biology and computers to enhance the way in which systems are designed. Artificial Immune System (AIS) is a Computational Intelligence strategy dependent on an organically enlivened computational system that can be utilized for taking care of complex computational issues. It tends to be seen that AIS is an incredibly various locale of research, going from the modeling immune systems to complex algorithms for specific applications. This paper exhibits an exhaustive survey of different frameworks developed in the artificial immune system and its application. Reviews of frameworks in AIS are uncommon and henceforth this paper gives an inside out audit of progressing research and challenges in AIS. We start by presenting AIS and give a thorough survey of different systems in AIS and its application in anomaly detection. We investigate the utilization of AIS in the Intrusion Detection System named the Cyber Immune System(CIS) and compares various AIS works applied to CIS. We conclude with various future extensions in the area of AIS research. 2019 by Advance Scientific Research. This is an open-access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) -
Artificial Ecosystem-Based Optimization for Optimal Location and Sizing of Solar Photovoltaic Distribution Generation in Agriculture Feeders
In this paper, an efficient nature-inspired meta-heuristic algorithm called artificial ecosystem-based optimization (AEO) is proposed for solving optimal locations and sizes of solar photovoltaic (SPV) systems problem in radial distribution system (RDS) towards minimization of grid dependency and greenhouse gas (GHG) emission. Considering loss minimization as main objective function, the location and size of solar photovoltaic systems (SPV) are optimized using AEO algorithm. The results on Indian practical 22-bus agriculture feeder and 28-bus rural feeders are highlighted the need of optimally distributed SPV systems for maintaining minimal grid dependency and reduced GHG emission from conventional energy (CE) sources. Moreover, the results of AEO have been compared with different heuristic approaches and highlighted its superiority in terms of convergence characteristics and redundancy features in solving the complex, nonlinear, multi-variable optimization problems in real time. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Artificial Butterfly Optimizer Based Two-Layer Convolutional Neural Network with Polarized Attention Mechanism for Human Activity Recognition
Human activity recognition (HAR) is a focal point of study in the realms of human perception and computer vision due to its widespread applicability in various contexts, such as intelligent video surveillance, ambient assisted living, HCI, HRI, IR, entertainment, and intelligent driving. With the prevalence of deep learning techniques for image classification, researchers have shifted away from the labor-intensive practice of hand-crafting in favor of these methods in HAR. However, Convolutional Neural Networks (CNNs) face challenges such as the receptive field problem and limited sample issues that remain unsolved. This paper introduces a two-branch convolutional neural network for HAR classification, incorporating a polarized full attention method to address the aforementioned issues. The Artificial Butterfly Optimization (ABO) is employed for optimal hyper-parameter tuning. The proposed network utilizes twobranch CNNs to efficiently extract data, simplifying convolutional layers' kernel sizes to enhance network training and suitability for low-data settings. Feature extraction effectiveness is improved by implementing the one-shot assembly method. To amalgamate feature maps and provide global context, an enhanced full attention block called polarized full attention is utilized. Experimental results demonstrate the superiority of the proposed model in detecting human behaviors on the LoDVP Abnormal Behaviors dataset and the UCF50 dataset. Furthermore, the suggested model is adaptable to incorporate new sensor data, making it particularly valuable for real-time human activity identification applications. The Recall is 100 for the 1st dataset, 94 for the 2nd dataset, and 100 for the 3rd dataset, respectively. The F1-Score is 96.61836 for the 1st dataset, 96.90722 for the 2nd dataset, and 98.03922 for the 3rd dataset, respectively. 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/). All Rights Reserved.