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The Tracking of (Machine) Intelligences Evolution Using an Intelligence Catalogue
The purpose is to investigate the usage of capabilities that identify intelligence in the scientific discourse on AI, to track the evolution of the mythology around intelligence and how it appears in both people and computers across time. The form of a catalog, and covering various domains, including AI, intelligence capabilities, and related traits that are used to define intelligence were extracted from prior research in this area. Even if intelligence is still a nebulous, ill-defined term, examining and comprehending the language surrounding it could influence how we utilize it as well as how intelligent artifacts are made now and in the future. 2024 selection and editorial matter, Prof. (Dr.) Dorota Jelonek, Prof. (Dr.) Narendra Kumar, Prof. (Dr.) Mamta Chahar, Prof. (Dr.) Rusudan Kinkladze and Prof. (Dr.) Lilla Knop; individual chapters, the contributors. -
Assessing Human Stress Through Smartphone Usage
Stress occurs in a human being when they are faced with exigent situations in life. Assessing stress has been always challenging. Smartphones have become a part of everyones day-to-day activity in the present time. Considering humansmartphone interaction, sensing of stress in an individual can be assessed as todays youth spends most of their time with smartphones. Taking this into consideration, a study is carried out in this paper on assessing stress of an individual based on their interaction with the smartphone. In this work, humansmartphone interaction features, like swipe, scroll, and text input, are examined. Text input is incorporated by disabling the autocorrection and spelling checker features of the keyboard. Moreover, sensor data is used by Google activity recognition API to analyze the physical activity of the individual to assess the stress level. 2019, Springer Nature Singapore Pte Ltd. -
AI-Augmented FinTech Platforms for Real-Time Credit Risk and Supply Chain Financing in Smart Industries
A combination of Artificial Intelligence (AI) and FinTech platforms has transformed the financial services sector, specifically, real-time credit risk evaluation and supply chain financing of smart industries. The conventional models of credit assessment, based on the use of fixed financial information and manual processing, are ineffective in capturing the dynamic nature of the modern-day industrial process. This paper conducts empirical research on AI-enhanced FinTech applications and utilizes machine learning (ML), natural language processing (NLP), and multi-modal industrial data, such as financial data, IoT sensor data, and supply chain data, to enable better predictive models and decision processes. The study compares several models, such as ensemble learning and deep neural networks, to predict credit risk and maximize the financing. Findings show that this is highly improved with AUC scores more than 0.88 and reduction in decision latency up to 70 percent showing quicker more information-oriented and context-sensitive risk management. It offers practical implications in the design of AI-based financial solutions, which will allow making smarter credit decisions and allocating working capital in intelligent industries more effectively, and it also notes that AI can transform industrial FinTech ecosystems. 2026 IEEE. -
Federated and Explainable AI Models for Secure FinTech Transactions in Digital Manufacturing Supply Chains
Digital manufacturing supply chains are becoming increasingly dependent on inbuilt FinTech services to perform automated payments, invoicing, and settlements which presents sensitive financial and operational data to security and privacy threats. This article is an empirical paper concerning the application of Federated Learning (FL) and Explainable Artificial Intelligence (XAI) in securing FinTech transactions in decentralized manufacturing supply chains. The suggested framework will facilitate joint fraud and anomaly-related detection without exchanging raw data between supply-chain participants. Different privacy mechanisms such as client-level and secure aggregation are integrated to safeguard sensitive data and minimize the risks of inferences. Explainable AI methods are used such as SHAP, local surrogate models, to enable transparency and auditability as well as regulatory compliance. Experimental evidence has shown that federated models can attain almost centralized detection accuracy with much stronger privacy guarantees and explainability procedures can give insightful and interpretable information about model decisions. The paper identifies the trade-offs between accuracy, privacy, and computational overhead and concludes that federated and explainable AI provides a convenient, secure, and compliant solution to FinTech-enabled digital manufacturing ecosystems. 2026 IEEE. -
Development of smart energy monitoring using NB-IOT and cloud
IoT-based applications are growing in popularity nowadays because they offer effective answers to numerous current problems. In this research, With the aim of decreasing human efforts for monitoring the power units and increasing users' knowledge of excessive electricity usage, an IoT-based electric metre surveillance system utilising an Android platform has been developed. With the help of an Arduino Uno and an optical sensor, the electric analyser pulse is captured. To reduce human mistake and the expense of energy usage, a low-cost wireless network of sensors for digital energy metres is implemented alongside a smartphone application that can autonomously read the metre of the unit. In this research, an intelligent power monitoring system with effective communication modules has been developed to make wise use of the electricity. The controller, NB-IoT connection module, and cloud are the three main components of an IOT-based smart energy metre system. The controller is essential for maintaining the functionality of each component. This solution reduces the need for human involvement in electricity maintenance by connecting energy metres to the cloud using an NB-IoT communication module. The IoT-based metre reading system in the proposed work is created to monitor and analyse the metre reading, and the service provider can cut off the source of electricity whenever the customer fails to pay the monthly bill. It also eliminates the need for human intervention, provides accurate metre reading, and guards against billing errors. The proposed SPM improves the overall accuracy ranges of 7.42, 27.83, and 20% better than DR, OREM, and SLN respectively. 2023 -
The role of big data in predicting consumer behavior
Consumer behavior prediction is a significant task, and it is a prerequisite for marketing activities. Regardless of the product type/market type, predicting consumer behavior plays a vital role in determining the target market. The activities involved in identifying a target market include the tasks of analyzing the offerings, conducting market research, identifying market segments to create consumer profiles, and assessing the competition. In order to complete all four tasks mentioned above, it needs to have comprehensive and precise data/dataset in hand. It also means that the data/fact is the primary source of predicting consumer behavior. In today's digital world, sources of source (data) are multifold. During the process of data collection, if the repository is accepting data from such sources, then all five "V" (Volume, Velocity, Variety, Veracity, and Value) of data should be considered. The role of big data in predicting consumer behavior is inevitable. Machine learning models shall be deployed to analyze data from big data. In this chapter, benchmark datasets, and machine learning, are used to demonstrate the usage of artificial intelligence in analyzing, forecasting, and predicting consumer behavior. Before concluding the chapter, the performance of algorithms is evaluated and compared to find the most suitable models for predicting consumer behavior. Benchmark datasets are used in this chapter to represent the role of big data in predicting consumer behavior. 2023 Nova Science Publishers, Inc. All rights reserved. -
Assessing Academic Performance Using Ensemble Machine Learning Models
Artificial Intelligence (AI) shall play a vital role in forecasting and predicting the academic performance of students. Societal factors such as family size, education and occupation of parents, and students' health, along with the details of their behavioral absenteeism are used as independent variables for the analysis. To perform this study, a standardized dataset is used with data instances of 1044 entries and a total of 33 unique variables constituting the feature matrix. Machine learning (ML) algorithms such as Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), LightGBM, and Ensemble Stacking (ES) are used to assess the specified dataset. Finally, an ES model is developed and used for assessment. Comparatively, the ES model outclassed other ML models with a test accuracy of 99.3%. Apart from accuracy, other parameters of metrics are used to evaluate the performance of the algorithms. 2023 IEEE. -
Ensembled convolutional neural network for multi-class skin cancer detection
A skin cancer diagnosis is critically important in medical image processing. The role of dermoscopy and dermatologists is inevitable in skin cancer diagnosis. But, considering the time constraints on diagnosing patients on time, even medical experts need computer-assisted methods to automate the diagnosis process with a higher accuracy rate and with good performance. Such computer-assisted methods with induced artificial intelligence (AI) algorithms are gaining significance. The challenging task of medical image processing is finding benign/malignant pigmented skin lesions after the input image of patients. To identify this difference, AI-based classification algorithms shall be deployed. During the implementation of such algorithms, several performance aspects are evaluated. Once the best such algorithm is identified and evaluated for its performance attributes, it shall be deployed to assist dermatologists. This book chapter explains such a novel multiclass skin cancer classification algorithm. The proposed algorithm uses the best of the attributes and parameters of a deep convolutional neural network (CNN) to give the best-ever enactment among similar existing algorithms. The result achievement of the developed deep CNN based multi-class skin cancer classification algorithm (DCNN-MSCCA) is demonstrated using the HAM10000 dataset. To establish the significance of the developed algorithm, the performance parameters of the DCNN-MSCCA are compared with a few existing significant algorithms. The maximum accuracy of DCNN-MSCCA in predicting the exact multi-class skin cancer is 95.1%. This book chapter explains the implementation details of DCNN-MSCCA using python and libraries supporting CNN. 2024 River Publishers. -
An investigation and analysis on automatic speech recognition systems
A crucial part of a Speech Recognition System (SRS) is working on its most fundamental modules with the latest technology. While the fundamentals provide basic insights into the system, the recent technologies used on it would provide more ways of exploring and exploiting the fundamentals to upgrade the system itself. These upgrades end up in finding more specific ways to enhance the scope of SRS. Algorithms like the Hidden Markov Model (HMM), Artificial Neural Network (ANN), the hybrid versions of HMM and ANN, Recurrent Neural Networks (RNN), and many similar are used in accomplishing high performance in SRS systems. Considering the domain of application of SRS, the algorithm selection criteria play a critical role in enhancing the performance of SRS. The algorithm chosen for SRS should finally work in hand with the language model conformed to the natural language constraints. Each language model follows a variety of methods according to the application domain. Hybrid constraints are considered in the case of geography-specific dialects. 2024 by author(s). -
Hybrid cryptography security in public cloud using TwoFish and ECC algorithm
Cloud computing is a structure for rendering service to the user for free or paid basis through internet facility where we can access to a bulk of shared resources which results in saving managing cost and time for large companies, The data which are stored in the data center may incur various security, damage and threat issues which may result in data leakage, insecure interface and inside attacks. This paper will demonstrate the implementation of hybrid cryptography security in public cloud by a combination of Elliptical Curve Cryptography and TwoFish algorithm, which provides an innovative solution to enhance the security features of the cloud so that we can improve the service thus results in increasing the trust overthe technology. 2019 Institute of Advanced Engineering and Science. -
Artificial Intelligence in Data-Driven Analytics for the Personalized Healthcare
Among the various developments in progress over the last decade, we have seen the generous growth of information investigation to take care of, plan, and use a lot of information beneficially. Be that as it may, because the analysis of evidence will only operate for authentic information and have findings as predefined by individuals, explicit principle-based calculations have been developed to broaden the investigation of information, 'Which is usually referred to as 'AI'. AI didn't expect PCs to be personalized unambiguously, which is a definite bit of leeway. In order to break down information and construct complicated equations to foresee models, which was called prescient analysis, AI was then joined with information inquiry. A set of laws characterized by persons, known as prescient equations, drive the prescient inquiry, and are used to break down genuine knowledge in order to predict potential outcomes. 2021 IEEE. -
Implantable Chip Revolutionizing Early-Stage Liver Cancer Detection with Advanced Diagnosis System
Millions of people die from cancer annually. Advanced metastatic cancers may not respond to traditional therapy. The importance for early diagnosis is highlighted by the difficulty of treating cancers in later stages. Enhancing patient outcomes using tissue-engineered cancer diagnosis and therapy is gaining popularity. Cancer and associated immune problems burden healthcare systems, making efficient, high-throughput drug development strategies essential. Thus, implanted chips may solve these issues. A revolutionary technique for early liver cancer identification is the Machine Learning-based Liver Cancer Diagnosis System (ML-LCDS). K-Nearest Neighbour (KNN) identifies liver tumors precisely in ML-LCDS. The performance evaluation reports sensitivity=97.2%, specificity=91.3%, precision=93.5%, FPR=8.7%, and accuracy=94.1%, computed from the confusion matrix derived through 10-fold cross-validation. Experimental findings validate its consistent performance, establishing ML-LCDS as an efficient and reliable diagnostic tool for early-stage liver cancer detection. The Author(s) 2025. The text of this article is open access and licensed under a Creative Commons Attribution 4.0 International License. -
Inplane Lateral Load Behaviour of Masonry Walls
Masonry is one of the commonly used construction technology both in urban and rural areas. In this paper the in-plane behaviour of masonry walls is analytically studied considering existing closed form equations. Previous studies have proven that the lateral load behaviour mainly depends on the aspect ratios (h/L) as well as the axial loads. From this analysis the governing failure is determined and the lateral load versus lateral deflection curve is plotted for various percentages of axial loads. This graph gives the ductility of the wall. This concept is further applied to a simple masonry structure and the push over curve is plotted. 2020, Springer Nature Switzerland AG. -
A Review on Influence of Cutting Fluid on Improving the Machinability of Inconel 718
Nickel-based superalloys are widely used in the production and manufacturing sectors that require processes or applications that endure or operate at very high superheating temperatures. With the properties of high tensile strength, high melting point, and lightweight structural arrangement of molecules within the alloy material composition makes it more suitable for industrial utilization in aerospace industries and marine applications. This review paper discusses the use of various coolant lubricants that improves the machinability of Inconel 718 based on parameters such as surface roughness and tool wear under the influence of cutting speed, feed rate, and depth of cut. The machine used for analysis is CNC milling machine which will be used for experimentation using ceramic inserts as end milling tool. Various cooling techniques such as hybrid cooling, flood emulsion cooling, minimum quantity lubrication, and cryogenic cooling are being summarized in this paper from various experimentations and conclusions of other authors. On the basis of review, the hybrid cooling technique is found to be better than other cooling techniques because of its ability to obtain long tool life and smoother surface finish on the workpiece. With the use of these reviewed data, further research for finding a more compatible and effective cooling lubricant has to be done by experimentation in order to obtain an improved machining process for Inconel 718 material. 2020, Springer Nature Singapore Pte Ltd. -
Deep Learning Based Multi Constraint Hybrid Optimization Algorithm for Transshipment-Based Inventory Routing with Dynamic Demands
The Inventory-Routing Problem (IRP) is considered a major issue in supply chain management as it comprises two areas: vehicle routing and inventory control. The existing techniqueswere unable to incorporate location details for enhancing the decision-making and it failed to consider the uncertainty of the demands. Hence to solve this issue, a Snake Artificial Ecosystem Optimization (SAEO) algorithm is proposed in this paper. The SAEO algorithm is developed to address the transshipment IRP with dynamic demands by combining the AEO model and SO to enhance the optimizer's performance. Further, a penalty strategy is proposed, where Deep Quantum Neural Network (DQNN) is employed for calculating the penalty for verifying the feasibility of the solution generated in case of violations in model constraints. In addition, the efficiency of the proposed SAEO-DQNN technique is examined by considering metrics, like transportation cost, transshipment cost, and total cost, and it achieved improved values of 0.391, 0.518, and 1.012 when compared to existing techniques such as Genetic Algorithm with Deep Reinforcement Learning (GA + Deep RL) and Kernel Search Multi-vehicle IRP (KSMIRP). The Author(s) 2024. -
WSETO: wild stock exchange trading optimization algorithm enabled routing for NB-IoT tracking system
The Narrowband Internet of Things (NB-IoT) communication plays a significant role in the IoT due to the capability of generating broad exploration with the usage of limited power. Over the past few years, the Low Power Wide Area Networks (LPWAN) have been efficient in the data acquisition and remote monitoring area however they failed to generate high data rates, low latency, and the consumption of low power. To solve these problems, NB-IoT technology has developed in long-term asset tracking and it replaces the Global Positioning System (GPS) with its ubiquitous coverage. In this research, the Wild Stock Exchange Trading Optimization technique (WSETO) is proposed for a routing-based NB-IoT tracking system. The WSETO is the combination of the Wild Geese Algorithm (WGA) and SETO. By employing WSETO, the routing to the relevant target location is established effectively. The existing techniques like Low Power Asset Tracking of NB-IoT (LoPATraN), Monitoring system based on NB-IoT and BeiDou System/GPS (BDS/GPS), and Narrowband Physical Uplink Shared Channel (NPUSCH) are used to compare the WSETO approach. In rounds with a value of 2000, the WSETO demonstrates a superior location error of 0.001 in comparison to existing methods such as LoPATraN, a monitoring system utilizing NB-IoT and BDS/GPS, as well as NPUSCH. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Tracking and Localization of Devices - An IoT Review
S everal IoT applications have immediate impacts on daily lives. The notion of "connected life, which includes IoT has been discussed. Apps that rely on localization are also featured. IoT is originally used to determine the precise position of things, animals, and people. The second tracks everyone and everything that's on the move, including pets, kids, and the elderly people. Localization and tracking are integral parts of security and surveillance systems in interconnected homes. This study reviews the state-of-the-art IoT-based localization and tracking approaches and outlines the key technical aspects, and contrast localization initiatives based on Internet of Things (IoT) with those that do not show how they might be used in a variety of contexts. It is now well established that localization and tracking methods based on the Internet of Things (IoT) are more pervasive and accurate than their predecessors. 2023 IEEE. -
Deep Learning Based Multi Constraint Hybrid Optimization Algorithm for Transshipment-Based Inventory Routing with Dynamic Demands
The Inventory-Routing Problem (IRP) is considered a major issue in supply chain management as it comprises two areas: vehicle routing and inventory control. The existing techniqueswere unable to incorporate location details for enhancing the decision-making and it failed to consider the uncertainty of the demands. Hence to solve this issue, a Snake Artificial Ecosystem Optimization (SAEO) algorithm is proposed in this paper. The SAEO algorithm is developed to address the transshipment IRP with dynamic demands by combining the AEO model and SO to enhance the optimizer's performance. Further, a penalty strategy is proposed, where Deep Quantum Neural Network (DQNN) is employed for calculating the penalty for verifying the feasibility of the solution generated in case of violations in model constraints. In addition, the efficiency of the proposed SAEO-DQNN technique is examined by considering metrics, like transportation cost, transshipment cost, and total cost, and it achieved improved values of 0.391, 0.518, and 1.012 when compared to existing techniques such as Genetic Algorithm with Deep Reinforcement Learning (GA + Deep RL) and Kernel Search Multi-vehicle IRP (KSMIRP). The Author(s) 2024. -
Indoor Localization and Tracking with IoT: A Critical Survey of Technologies, Challenges, and Future Trends
Indoor localization and tracking have been important areas of research throughout the past 10 years, driven by the expanding Internet of Things (IoT) technologies. The shortcomings of conventional GPS in indoor environments have called for the development of replacement localization methods. This paper presents a methodical review of IoT-enabled indoor localization techniques covering both well-known technologies such as Bluetooth Low Energy (BLE), Radio-Frequency Identification (RFID), Ultra-Wideband (UWB), and Wi-Fi fingerprinting, as well as newer approaches such as Visible Light Communication (VLC). We critically evaluate these technologies by way of a comprehensive analysis of modern research and case studies, emphasizing significant performance criteria such as accuracy, scalability, and energy efficiency as well as pragmatic concerns such as cost and security. Our work looks at field trends still in development, highlights significant gaps and problems, and integrates the current state of the art. We also stress potential application fields - such as smart homes, healthcare, and industrial automation - that stand to benefit significantly from advances in indoor localization. Finally, we outline future research intended to address current limitations, including the need of higher accuracy in complex environments and more robust security measures. 2025 IEEE. -
Healthcare Metaverse
Discussions regarding metaverse technologies are happening all over the place, from universities to business tycoons. A lot of people are thinking about how to make their apps work better in the metaverse. To better serve their patients, more and more healthcare firms are embracing the metaverse. In this research, healthcare metaverses are examined. We show how to improve healthcare services in the metaverse and increase patient use cases by using safer approaches to managing chronic diseases, mental health, and fitness. With the advent of digital twins, artificial intelligence (AI), immersive technologies, the Internet of Things (IoT), and blockchain (BC), new possibilities in healthcare are emerging in the metaverse. These innovations have the potential to change the way people perceive healthcare, save costs, and enhance patient outcomes. Healthcare may be revolutionized by using AI and BC technology to sift through massive amounts of data and develop individualized treatment regimens. But IoT devices gather vital data for patient therapy instantly. The healthcare system and people's lives throughout the globe may both benefit from these concepts coming together. The recommendations made in this article should be adhered to ensure that digital procedures continue to benefit customers. 2026 Scrivener Publishing LLC.