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Adoption of knowledge-graph best development practices for scalable and optimized manufacturing processes
Using data analytics to properly extracting insights that are in-line to the enterprises strategic goals is crucial for the business sustainability. Developing the most fitting context as a knowledge graph that answer related businesses questions and queries at scale. Data analytics is an integral main part of smart manufacturing for monitoring the production processes and identifying the potentials for automated operations for improved manufacturing performance. This paper reviews and investigates the best development practices to be followed for industrial enterprise knowledge-graph development that support smart manufacturing in the following aspects: Decision for intelligent business processes, data collection from multiple sources, competitive advantage graph ontology, ensuring data quality, improved data analytics, human-friendly interaction, rapid and scalable enterprise's architectures. Successful digital-transformation adoption for smart manufacturing as an enterprise knowledge-graph development with the capability to be transformed to data fabric supporting scalability of smart manufacturing processes in industrial enterprises. 2023 -
Adoption of Sustainable Digital Technologies in Industry 4.0
We are living in a society that has been engulfed with growing technology, and the integration of it has become such an important part of our lives that it is scary to think of our daily lives without mobile phones, internet, or smart gadgets. Industry 4.0, briefly, means using new age technologies such as cloud computing, artificial intelligence, machine learning, Internet of Things, and big data in the different real-world applications of manufacturing, processing, and distribution of goods and services. Industry 4.0 involves making use of smart factories and technologies to minimize waste and to gain an absolute advantage in the development process. We already know the different use cases of these technologies and how these things help in lessening our workload, so it seems logical to apply them to broader aspects of our daily lives. Technologies mitigate our workload and improve efficiency. We have seen that these technologies are proving useful in different spheres of economics, with the help of new decision-making processes, model predictions, and even to improve healthcare. Through the scope of this chapter, we would shed light on how these different technologies are being incorporated and how these would help in stabilizing industry by its constant integration. 2024 selection and editorial matter, Vandana Sharma, Balamurugan Balusamy, Munish Sabharwal, and Mariya Ouaissa. -
Adsorption and storage of hydrogen- A computational model approach
Due to the imperative global energy transition crisis, hydrogen storage and adsorption technologies are becoming popular with the growing hydrogen economy. Recently, complex hydrides have been one of the most reliable materials for storing and transporting hydrogen gas to various fuel cells to generate clean energy with zero carbon emissions. With the ever-increasing carbon emissions, it is necessary to substitute the current energy sources with green hydrogen-based efficient energy-integrated systems. Herein, we propose an input-output model that comprehends complex hydrides such as lithium and magnesium alanates, amides and borohydrides to predict, estimate, and directly analyse hydrogen storage and adsorption. A critical and thorough comparative analysis of the respective complex hydrides for hydrogen adsorption and storage is discussed, elucidating the storage applications in water bodies. Several industrial scale-up processes, economic analysis, and plant design of hydrogen storage and adsorption approaches are suggested through volumetric and gravimetric calculations. 2024 Elsevier Inc. -
Adsorptive capacity of PANI/Bi2O3 composite through isotherm and kinetics studies on alizarin red
Adsorption offers numerous advantages for eliminating organic pollutants such as dyes, making it a valuable method for water treatment. Polyaniline/Bi2O3 (PANI/Bi2O3) nanocomposite is synthesized from aniline by the chemical oxidative polymerization method. The sample shows a high positive surface charge density as seen from zeta potential analysis. X-ray Diffraction analysis, FTIR analysis, UVvis spectroscopy technique, thermogravimetric analysis, BET N2 Adsorption-desorption analysis, DLS, and zeta potential analysis are the tools employed to characterize the PANI/Bi2O3 nanocomposite. The impact of PANI/Bi2O3 on the outcome of adsorption is confirmed by comparing the composite with pristine Bi2O3 and PANI. The effect of various factors like time, temperature, initial dye concentration, and varying pH on the adsorption efficiency is studied. A maximum adsorption efficiency of 95 % is observed when 100 mg of PANI/Bi2O3 nanocomposite is utilized for a duration of 100 min. The adsorption efficiency increases at higher temperatures, and a maximum adsorption efficiency is observed at a pH of 11.4. The adsorption isotherms proposed by Freundlich and Langmuir are examined to confirm the adsorption mechanism, which entails the creation of a single layer of dye molecules on the adsorbent's surface. Analysis of kinetic parameters indicates that the reaction follows pseudo-second-order adsorption kinetics. The composite produced demonstrates effectiveness as an adsorbent for removing harmful organic pollutants from water sources. 2024 Elsevier B.V. -
Adsorptive removal of Cr (VI) using mesoporous iron-aluminum oxyhydroxide-polyvinyl alcohol self-supporting film: Kinetics, optimization studies and mechanism
Over the past decades, the disposal of heavy metals like Cr(VI) from industries had an adverse effect on the environment, thus making it a topic of particular interest. In this context, mesoporous Aluminum oxyhydroxide-polyvinyl alcohol self-supporting films were synthesized, and different transition metals (V, Fe, Co, Ni and Cu) were incorporated by an eco-friendly route, and their adsorptive capacity towards Cr (VI) was studied. The composite mesoporous film with iron, aluminum oxyhydroxide and PVA was more efficient adsorbent than other transition metal incorporated aluminum oxyhydroxide films. The surface and chemical properties of the film were confirmed by pXRD, FTIR, Raman Spectra, BET-Surface area, BJH, SEM and Optical Profilometry. Furthermore, the effect of different parameters that impact the adsorption capacity towards Cr (VI) is discussed, including adsorbent load, contact time, solution pH, temperature, and initial concentration. A detailed investigation of the film before and after the adsorption of Cr (VI) using different characterization techniques is investigated in detail. The kinetic studies and adsorption isotherms are studied, and a suitable mechanism has been proposed for Cr (VI) removal. The synthesized films possess potential advantages like cost-effectiveness, eco-friendly nature, reusability, and higher removal efficiency towards the removal of Cr (VI) from an aqueous solution. 2023 Elsevier Ltd -
Adsorptive removal studies of Rhodamine B by PEG capped polyaniline/TiO2/CuO composite
The availability of pure and fresh water is the prime need of human beings for survival. The fact that the rate of water pollution is alarmingly increasing is making scientists think of ways to minimize the pollution effects. Among the numerous techniques, adsorption is the most cost-effective and easy method for removing pollutants from water bodies. In this study, a ternary PEG capped polyaniline/TiO2/CuO composite with excellent surface area is synthesized, and its adsorption studies carried out using Rhodamine B, one of the strongest organic pollutants. The characterization studies of the prepared composites have been done using XRD, FT-IR, HR-TEM, DLS, zeta potential, BET, and XPS analyses. Isotherm, kinetics, and thermodynamic studies of adsorption have been done for the prepared composite to evaluate its adsorptive efficiency. The adsorption studies of RhB dye using the synthesized composite followed the Langmuir adsorption isotherm. The kinetics study for the adsorption process indicates that pseudo second order kinetics fits best with the adsorption process taking place on the PANI/TiO2/CuO composite. The thermodynamic studies reveal the spontaneity of the process and the exothermic behavior at lower temperatures. The results prove the efficiency of the synthesized adsorbent towards environmental remediation studies. 2023 Elsevier Ltd -
Advance Data Ingestion Framework - Integration, Processing, Transformation, and Loading
The research introduces a new concept known as the Advanced Data Ingestion Framework, which is aimed at enhancing the process of getting into stored information through some intelligent methods like data preprocessing, transformation and loading. By making use of Azure services, the platform considers distributed computing and parallel processing so that structured as well as unstructured data can be incorporated from various origins without any difficulty. To begin with, the proposed framework starts with setting up scalable Azure infrastructure and integrating SAP S4/HANA for secure and efficient data transfer purposes. Within Azure Data Factory the ingestion occurs while Delta Lake ensures proper housekeeping & integrity within the system. It includes creating Power BI dashboards which allow users to see patterns easily and make better decisions based on what they know or can learn. The study brings out the flaws of current data input solutions and emphasizes the urgent requirement for a highly scalable low latency system that can support real time data processing efficiently. It tests the framework under different performance environments showing that it can effectively manage modern data within it. Finally, there is discussion about future improvements such as incorporating more sophisticated analytics or ML models thereby strengthening the decisionmaking process based on available facts. 2025 IEEE. -
Advanced Applications of Python Data Structures and Algorithms
Data structures are essential principles applicable to any programming language in computer science. Data structures may be studied more easily with Python than with any other programming language because of their interpretability, interactivity, and object-oriented nature. Computers may store and process data at an extraordinary rate and with outstanding accuracy. Therefore, it is of the utmost importance that the data is efficiently stored and is able to be accessed promptly. In addition, data processing should take as little time as feasible while maintaining the highest possible level of precision. Advanced Applications of Python Data Structures and Algorithms assists in understanding and applying the fundamentals of data structures and their many implementations and discusses the advantages and disadvantages of various data structures. Covering key topics such as Python, linked lists, datatypes, and operators, this reference work is ideal for industry professionals, computer scientists, researchers, academicians, scholars, practitioners, instructors, and students. 2023 by IGI Global. All rights reserved. -
Advanced Approaches for Hate Speech Detection: A Machine and Deep Learning Investigation
The prevalence of online social media platforms has led to an alarming rise in the frequency of cyberbullying and hate speech. This study uses a variety of machine-learning approaches and deep- learning algorithms to identify hate speech. The goal is to create a thorough and successful method for locating and categorizing hate speech on online networks. Our suggested approach intends to deliver a comprehensive solution to address the urgent problem of cyberbullying and hate speech in the digital sphere by leveraging the strength of these cutting-edge techniques. We work to make social media users' online experiences safer and more welcoming by identifying and addressing such harmful online actions. Through rigorous experimentation, we evaluate the efficacy of these methodologies, ultimately revealing that the Bidirectional Gated Recurrent Unit (Bi-GRU) outperforms the other employed techniques. The Bi-GRU model demonstrates superior hate speech detection capabilities, substantiated by robust performance metrics. This research contributes to the field by providing empirical evidence that deep learning models, such as Bi-GRU, can significantly advance hate speech detection accuracy. The findings underscore the potential of leveraging advanced neural architectures in the pursuit of fostering a more inclusive and respectful digital space. 2024 IEEE. -
Advanced Botnet Detection Using Hybrid Machine Learning Models
The improvement of computer network systems, cyberattacks that take advantage of system flaws have increased, resulting in significant monetary losses, business interruptions, harm to one's reputation, and legal repercussions. This research examines nine attack types, those are Fuzzers, Shellcode, Generic, Worms, Analysis, Normal, DoS, Exploits, Backdoor, and Reconnaissance. Botnet assaults are attacks in which a single operator controls several networked devices. The research study examines several models, such as Random Forest, XGBoost Classifier, Logistic Regression, and Decision Tree, to improve detecting skills. By utilizing the advantages of both methods, the suggested ERFwXGBoost (Enhanced Random Forest with XGBoost) model, which blends Random Forest and XGBoost, exhibits remarkable performance. Notably, first they analyze the accuracy, then precision value is also measured, third will measure recall value, and then finally F1 score of the ERFwXGBoost model are all impressively 0.98. In addition to outperforming individual models, our hybrid technique offers a reliable and effective way to detect different kinds of botnet attacks. The research emphasizes how well these models work together to boost overall system security against advanced cyber threats and greatly increase detection accuracy. 2025 IEEE. -
Advanced Cervical Lesion Detection using Deep Learning Techniques
Cervical cancer has been one of the common causes for mortality by cancer in women across the world. But there are currently not enough skilled colposcopists, and the training process is drawn out. This implicates that there is a significant scope for artificial intelligence based computational models for segmentation of colposcope images. This paper proposes a segmentation network to accurately segment the cervix region and acetowhite lesions in a cervigram. This research can lay a foundation for research aiming to classify the cervix malignancy using AI. The method performed with a precision of 0.73870.1541, accuracy of 0.9291, recall of 0.79120.1439, a dice score of 0.74310.1506 and specificity of 0.95890.0131. The results prove that the model is reliable and robust. 2024 IEEE. -
Advanced Computational Method to Extract Heart Artery Region
Coronary artery disease, also known as coronary heart disease, is the thinning or blockage of heart arteries, which is generally caused utilizing the build-up of fatty material called plaque. The coronary angiogram test is currently the most utilized method for identifying the stenosis status of arteries in the heart. The objective of the proposed hybrid segmentation method is to extract the artery region of the heart from angiogram imagery. Numerous angiogram video clips have been considered in the dataset in this research work. These video clips were acquired from a healthcare center with the due consent of patients and the concerned healthcare personnel. Most angiogram videos consist of unclear images, or the contents are generally not clear, and medical experts fail to acquire accurate information about the damages or blocks formed in arteries due to the same reason. A hybrid computational method to extract well-defined images of heart arteries using Frangi and motion blur features from angiogram imagery has been proposed to address this issue. Fifty patients' information has been used as the dataset for experimentation purposes in this research work. The enhanced Frangi filter is used on the dataset to obtain edge information to enhance the input image based on the Hessian matrix. Further, the motion blur helps in automatically tracking/tracing the pixel direction using the optical flow method. In this method, the complete structure of the artery is extracted. The results, when compared to the existing methods, have proven to be novel and more optimal. 2022 Seventh Sense Research Group. -
Advanced electrochemical detection and profiling of the antihypertensive drug atenolol via a SPION-activated carbon nanocomposite interface
This study reports the synthesis of superparamagnetic iron oxide nanoparticles (SPIONs), activated carbon (AC) and SPION-AC nanocomposites using a simple hydrothermal method. Characterization of the synthesized materials includes dynamic light scattering, X-ray diffraction, field emission scanning electron microscopy, high resolution transmission electron microscopy, and vibrating sample magnetometry, along with electrochemical characterization studies such as electrochemical impedance spectroscopy. Among the SPION-AC nanocomposites, SPION-15%AC was employed to modify a glassy carbon electrode (GCE). The synergistic interaction between SPION and AC significantly enhanced the electrochemical properties of the system, leading to the development of a highly efficient platform for the detection of the antihypertensive drug atenolol (ATN) in commercial tablet samples. The sensor demonstrated excellent performance, with a linear detection range from 1.21 ?M to 285 ?M. With a low detection limit (LOD) of 0.401 ?M, the sensor offers precise quantification of ATN, making it a promising tool for improving patient care. High selectivity, reproducibility, and excellent recovery in complex pharmaceutical matrices further highlight the potential of this sensor for biomedical and clinical applications. 2025 RSC. -
Advanced Electrochemical Detection of 2,4-dichlorophenol in Water with Molecularly Imprinted Chitosan Stabilized Gold Nanoparticles
2,4-Dichlorophenol (2,4-DCP) is a hazardous chemical that can be passed down to offspring. Because 2,4-DCP degrades slowly and can be passed down to future generations, its a pesticide that needs to be continuously monitored and managed. With the use of chitosan-stabilized AuNPs on a glassy carbon electrode and the molecular imprinting technique, an effective electrochemical sensor has been built for the selective determination of 2,4-DCP in different aqueous samples. The analytes electroactive surface area and number of interaction sites are both increased by the AuNPs. The formulated AuNPs were characterized using several material characterization techniques. Molecularly imprinted nanomaterials provided the selectivity against other interfering chlorophenols. With a detection limit of 6.33 nM and a broad linear dynamic range of 21.09 to 310 nM, 2,4-DCP was found using differential pulse voltammetry. Without interference from structural analogs, the sensor was effectively evaluated in a variety of contaminated water samples. 2024 The Electrochemical Society (ECS). Published on behalf of ECS by IOP Publishing Limited. All rights, including for text and data mining, AI training, and similar technologies, are reserved. -
Advanced electrochemical performance of N-Ti3C2/MnO2 MXene as a promising electrode for energy storage
In this study, we demonstrate a simple and efficient two-step synthetic strategy to design a high-performance N-Ti3C2/MnO2 composite for energy storage application. Nitrogen doping alters the electronic structure of electrode materials and enhances pseudocapacitance. N-Ti3C2 serves as a supporting substrate for MnO2, boosting the active surface area by preventing Ti3C2 layer stacking. Benefitting from the collaborative contribution and synergistic interaction within this multicomponent system, N-Ti3C2/MnO2 results in exceptional specific capacitance of 2107.1 Fg?1 at 1 Ag?1. It also exhibits a low internal resistance and maintains a capacitive retention of 94% over 3000 cycles. The asymmetric capacitor also delivers an energy density of 117.1 Whkg?1 at a power density of 1290.1 Wkg?1. This work presents a straightforward method for modifying Ti3C2 through nitrogen doping and the insertion of MnO2 as an interlayer spacer to enhance electrochemical performance. Qatar University and Springer Nature Switzerland AG 2024. -
Advanced electrochemical performance of N-Ti3C2/MnO2 MXene as a promising electrode for energy storage
In this study, we demonstrate a simple and efficient two-step synthetic strategy to design a high-performance N-Ti3C2/MnO2 composite for energy storage application. Nitrogen doping alters the electronic structure of electrode materials and enhances pseudocapacitance. N-Ti3C2 serves as a supporting substrate for MnO2, boosting the active surface area by preventing Ti3C2 layer stacking. Benefitting from the collaborative contribution and synergistic interaction within this multicomponent system, N-Ti3C2/MnO2 results in exceptional specific capacitance of 2107.1 Fg?1 at 1 Ag?1. It also exhibits a low internal resistance and maintains a capacitive retention of 94% over 3000 cycles. The asymmetric capacitor also delivers an energy density of 117.1 Whkg?1 at a power density of 1290.1 Wkg?1. This work presents a straightforward method for modifying Ti3C2 through nitrogen doping and the insertion of MnO2 as an interlayer spacer to enhance electrochemical performance. Qatar University and Springer Nature Switzerland AG 2024. -
Advanced Fraud Detection Using Machine Learning Techniques in Accounting and Finance Sector
Monetary fraud, which is a deceptive method for getting cash, has turned into a typical issue in organizations and associations as of late. Customary techniques like manual checks and reviews aren't extremely precise, are costly, and consume most of the day. Attempting to get cash by lying. With the ascent of simulated intelligence, approaches based on machine learning have become more well known. can be utilized shrewdly to track down fraud by dissecting an enormous number of monetary exercises information. Thus, this work attempts to give a systematic literature review (SLR) that ganders at the literature in a systematic manner. reviews and sums up the exploration on machine learning (ML)-based fraud recognizing that has proactively been finished. In particular, the review utilized the Kitchenham strategy, which depends on clear systems. It will then, at that point, concentrate and rundowns the significant pieces of the articles and give the outcomes. Considering the Few investigations have been finished to accumulate search systems from well-known electronic information base libraries. 93 pieces were picked, examined, and integrated in light of measures for what to incorporate and what to forget about. As the monetary world gets more confounded, robbery is turning into a more serious issue in the accounting and finance industry. Fraudulent activities cost cash, yet they likewise make it harder for individuals to trust monetary frameworks. To stop this danger, we want further developed ways of tracking down fraud straightaway. This theoretical gives an outline of how machine learning strategies are utilized to further develop fraud detection in accounting and finance. 2024 IEEE. -
Advanced hybrid SVPWM techniques for two level VSI
This paper brings an advanced class of hybrid SVPWM techniques for medium voltage drive applications with two-level inverter which employs multiple division of active vector time (MDAVT) switching sequences to reduce total harmonic distortion (THD) and switching loss. The proposed hybrid SVPWM techniques are categorised based on the principle of bus-clamping strategies. Multiple division active vector time (MDAVT) switching sequences are used in the proposed strategies. The newly developed MDAVT switching strategies produce PWM waveform for all odd and even pulse number and maintain the symmetry of the voltage waveform. This work compares different MDAVT switching sequences based on modulation index and location of the clamping position (zero vector changing angle) of a phase in a line cycle. The proposed techniques lead to the reduction in weighted total harmonic distortion of line voltage (Vwthd) as well as switching loss. The results point to the superior order of performance of the developed MDAVT sequences in the various ranges of operation of modulation index and power factor values. The superior harmonic performance and switching loss characteristics of the MDAVT PWM techniques over the conventional SVPWM is experimentally verifiedona415 V, 2 hp induction motor drive. 2021 Informa UK Limited, trading as Taylor & Francis Group. -
Advanced Load Balancing Min-Min Algorithm in Grid Computing
Framework figuring has turned into genuine distinctive to old supercomputing situations for creating parallel applications that bridle huge process assets. In any case, the quality acquired in building such parallel Framework mindful applications is over the ordinary parallel registering conditions. It tends to issues like asset disclosure, heterogeneous, adaptation to non-critical failure and assignment programming. Load balancing errand programming inconceivably indispensable downside in cutting edge lattice environment. Load balancing ways is normally utilized for the development of appropriated frameworks. Normally there is a three kind of stages related with Load compromise that is information arrangement, higher psychological process, learning Relocation. Take a gander at the impact of surveying on load assignment by contemplating a fundamental expense in limit. There are three completely hovered tallies to lift which put away the stack ought to be doled out to, pondering the framework action cost among get-togethers. These tallies utilize grouped data trade frameworks and an asset estimation framework to redesign the constrained air framework exactness of load adjusting. Springer Nature Switzerland AG 2020. -
Advanced Machine Learning Framework for Precision Rainfall Prediction for Jharkhand, India
Jharkhand, characterized by a substantial agricultural population predominantly reliant on rain-fed agriculture, faces significant challenges due to the erratic nature of precipitation. The study uses meteorological variables and historical rainfall data from the Jharkhand Space Agency Centre (JSAC) to predict rainfall with precision and resilience. Three supervised machine learning algorithms, Random Forest, K-Nearest Neighbour (KNN), and Ridge Regression, are employed to evaluate their performance across monsoon and non-monsoon periods. A novel algorithm is proposed for Jharkhand, offering better modularity and accuracy to predict the Rain Index. The results show the efficacy of these algorithms in capturing the temporal variability of rainfall in Jharkhand. The ensemble modeling model obtained an MSE score of 0.457, providing valuable insights into the viability and competence of machine learning algorithms for rainfall estimation. This research offers a valuable scope for policymakers, researchers, and stakeholders to formulate sustainable strategies to address climate variability and its impact on rain-fed agriculture. The study contributes significantly to meteorological research and offers valuable insights for policymakers, researchers, and stakeholders. 2025 IEEE.