Adsorption and storage of hydrogen- A computational model approach
Adsorption; Complex hydrides; Hydrogen storage; Low carbon hydrogen production
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.
Mittal H.; Kushwaha O.S.; Nadagouda M.; Hegde G.; Allen S.; M. Aminabhavi T.
ISSN: 139351; PubMed ID: 39004395; CODEN: ENVRA
https://doi.org/10.1016/j.envres.2024.119606 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198953796&doi=10.1016%2fj.envres.2024.119606&partnerID=40&md5=a6f2536607d532d874041e9c26308486
Mittal H., Center for Energy and Environment, School of Advanced Sciences, KLE Technological University, Karnataka, Hubballi, 580 031, India, University School of Chemical Technology, Guru Gobind Singh Indraprastha University, Delhi, Dwarka, 110 078, India; Kushwaha O.S., Chemical Engineering Department, Indian Institute of Technology, Madras, Chennai, 600036, India; Nadagouda M., Department of Mechanical and Materials Engineering, Wright State University, Dayton, 45435, OH, United States; Hegde G., Centre for Advanced Research and Development, CHRIST (Deemed to be University), Hosur Road, Bengaluru, 560 029, India; Allen S., School of Chemistry and Chemical Engineering, Queen's University, Belfast, BT9 5AG, United Kingdom; M. Aminabhavi T., Center for Energy and Environment, School of Advanced Sciences, KLE Technological University, Karnataka, Hubballi, 580 031, India, Korea University, Seoul, 02841, South Korea
Adsorptive capacity of PANI/Bi2O3 composite through isotherm and kinetics studies on alizarin red
Adsorption; Adsorption isotherm; Adsorption kinetics; Alizarin red; PANI/Bi2O3
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.
Joseph G.; Kalathiparambil Rajendra Pai S.D.; Varghese A.; Pinheiro D.; Mohan M.K.; Chundattu S.J.
Journal of Molecular Structure, Vol-1308
https://doi.org/10.1016/j.molstruc.2024.138095 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188537497&doi=10.1016%2fj.molstruc.2024.138095&partnerID=40&md5=1b3c165ec1d37102761604dff7e2bea3
Joseph G., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Kalathiparambil Rajendra Pai S.D., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Varghese A., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Pinheiro D., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Mohan M.K., Department of Sciences and Humanities, School of Engineering and Technology, CHRIST (Deemed to be University), Mysore Road, Kumbalagodu, Bangalore, 560074, India; Chundattu S.J., Department of Sciences and Humanities, School of Engineering and Technology, CHRIST (Deemed to be University), Mysore Road, Kumbalagodu, Bangalore, 560074, India
Adsorptive removal of Cr (VI) using mesoporous iron-aluminum oxyhydroxide-polyvinyl alcohol self-supporting film: Kinetics, optimization studies and mechanism
Bimetallic films; Cr (VI) removal; Heavy metal removal; Iron aluminum oxyhydroxide films
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
John B.C.; Viswambaram V.A.; Raj S.S.; Mankunipoyil S.A.
https://doi.org/10.1016/j.mtcomm.2023.105315 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145977733&doi=10.1016%2fj.mtcomm.2023.105315&partnerID=40&md5=b748b7a29ae002a23cd0cc43e2268dae
John B.C., Department of Sciences and Humanities, CHRIST (Deemed to be University), Bangalore, 560074, India; Viswambaram V.A., Department of Sciences and Humanities, CHRIST (Deemed to be University), Bangalore, 560074, India; Raj S.S., Department of Chemistry, CHRIST (Deemed to be University), Bangalore, 560029, India; Mankunipoyil S.A., Department of Chemistry, RV College of Engineering, Bangalore, India
Adsorptive removal studies of Rhodamine B by PEG capped polyaniline/TiO2/CuO composite
Adsorption isotherm; Adsorption kinetics; Adsorption thermodynamics; PANI/TiO2/CuO composite; Rhodamine B dye
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
https://doi.org/10.1016/j.mtcomm.2023.105739 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149435435&doi=10.1016%2fj.mtcomm.2023.105739&partnerID=40&md5=13ed1d9426a24562cd2d0cd3499f9aa8
Varghese A., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; K.R. S.D., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Pinheiro D., Department of Chemistry, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India
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.
Galety M.G.; Natarajan A.K.; Sriharsha A.V.
Advanced Applications of Python Data Structures and Algorithms, pp. 1-298.
Galety M.G., Department of Computer Science, Samarkand International University of Technology, Uzbekistan; Natarajan A.K., CHRIST University (Deemed), India; Sriharsha A.V., MB University, India
Advanced Approaches for Hate Speech Detection: A Machine and Deep Learning Investigation
Bi-GRU; classification; deep learning; Hate speech; machine learning; NLP; RNN
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.
Reghunathan A.; Singh S.; Gunavathi R.; Johnson A.
TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/TQCEBT59414.2024.10545188 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204489011&doi=10.1109%2fTQCEBT59414.2024.10545188&partnerID=40&md5=a933df9e38c3107705aeec06e4f0f29c
Reghunathan A., Christ (Deemed to Be University), School of Science, Lavasa, India; Singh S., Christ (Deemed to Be University), School of Science, Lavasa, India; Gunavathi R., Christ (Deemed to Be University), School of Science, Lavasa, India; Johnson A., Christ (Deemed to Be University), School of Science, Lavasa, India
Advanced Cervical Lesion Detection using Deep Learning Techniques
cervical cancer; cervigram; cervix; colposcope; Segmentation
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.
2024 1st International Conference on Communications and Computer Science, InCCCS 2024
Mukku L., Computer Science and Engineering, CHRIST(Deemed to be University), Bangalore, India; Thomas J., Computer Science and Engineering, CHRIST(Deemed to be University), Bangalore, India
Advanced Computational Method to Extract Heart Artery Region
Artery; Coronary Angiogram; Frangi Filter; Motion Blur; Segmentation; Stenosis
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.
International Journal of Engineering Trends and Technology, Vol-70, No. 6, pp. 366-378.
https://doi.org/10.14445/22315381/IJETT-V70I6P237 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134394346&doi=10.14445%2f22315381%2fIJETT-V70I6P237&partnerID=40&md5=1a1a24f6a0021d0699bddbbec2cda327
Kavipriya K., Department of Computer Science, Christ Deemed to be University, Karnataka, Bangalore, India; Hiremath M., Department of Computer Science, Christ Deemed to be University, Karnataka, Bangalore, India
Advanced Electrochemical Detection of 2,4-dichlorophenol in Water with Molecularly Imprinted Chitosan Stabilized Gold Nanoparticles
2; 4-DCP; chitosan; electrochemical sensors; gold nanoparticles; molecularly imprinted polymers
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.
Journal of the Electrochemical Society, Vol-171, No. 10
https://doi.org/10.1149/1945-7111/ad8312 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207321562&doi=10.1149%2f1945-7111%2fad8312&partnerID=40&md5=85cbe55eb9bb9bf1d7ca5274670b2e45
Ann Maria C.G., Department of Chemistry, CHRIST (Deemed to be University, Bengaluru, 560029, India; Varghese A., Department of Chemistry, CHRIST (Deemed to be University, Bengaluru, 560029, India; Nidhin M., Department of Chemistry, CHRIST (Deemed to be University, Bengaluru, 560029, India
Advanced electrochemical performance of N-Ti3C2/MnO2 MXene as a promising electrode for energy storage
Electrochemical; MnO2; Nitrogen doping; Supercapacitor; Ti3C2 MXene
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.
Mathew S.; Sunajadevi K.R.P.; Pinheiro D.; Saravanakumar B.
Mathew S., Department of Chemistry, Christ University, Karnataka, Bengaluru, 560029, India; Sunajadevi K.R.P., Department of Chemistry, Christ University, Karnataka, Bengaluru, 560029, India; Pinheiro D., Department of Chemistry, Christ University, Karnataka, Bengaluru, 560029, India; Saravanakumar B., Department of Physics, Dr. Mahalingam College of Engineering and Technology, Tamil Nadu, Pollachi, 642003, India
Advanced Fraud Detection Using Machine Learning Techniques in Accounting and Finance Sector
Accounting; Finance Sector; Fraud Detection; Machine Learning; Techniques
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.
Bhowte Y.W.; Roy A.; Raj K.B.; Sharma M.; Devi K.; Lathasoundarraj P.
Proceedings of 9th International Conference on Science, Technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 2024
Bhowte Y.W., Sinhgad Institute of Management And Computer Applications Narhe, Department of Mba, India; Roy A., Institute of Business And Computer Studies, Siksha 'o' Anusandhan, Deemed To Be University, Odisha, Bhubaneswar, 751030, India; Raj K.B., Institute of Public Enterprise, Department of Management Studies, Hyderabad, 500101, India; Sharma M., Thakur Institute of Management Studies And Research, Department of Finance, Mumbai, 400101, India; Devi K., Dav Autonomous College, Faculty In Commerce, Odisha, Titilagarh, India; Lathasoundarraj P., School of Business And Management, Christ University, Lavasa, Maharashtra, Pune, 412112, India
Advanced hybrid SVPWM techniques for two level VSI
induction motor drives; multiple Division Active Vector (MDAVT); Pulse width modulated inverters
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.
Vivek G.; Nair M.D.; Biswas J.; Barai M.
International Journal of Electronics, Vol-108, No. 9, pp. 1439-1464.
Vivek G., Propulsion Control Engineer, Bombardier Transportation, Vadodara, Gujarat, India; Nair M.D., Department of Electrical and Electronics Engineering, Karpagam College of Engineering, Coimbatore, India; Biswas J., Department of Computer Science, Christ University, Bangalore, India; Barai M., Department of Electrical Engineering, NIT Calicut, Calicut, India
Advanced Load Balancing Min-Min Algorithm in Grid Computing
Grid computing; Load balancing; Max-Min; Min-Min; Scheduling
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.
Raushan M.; Sebastian A.K.; Apoorva M.G.; Jayapandian N.
Lecture Notes on Data Engineering and Communications Technologies, Vol-31, pp. 991-997.
Springer Science and Business Media Deutschland GmbH
Raushan M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Sebastian A.K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Apoorva M.G., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Jayapandian N., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India
Advanced Machine Learning Techniques for Detecting Irregularities in Skin Lesion Borders: Enhancing Early Skin Cancer Detection
arc-based features; dermatogram analysis; ensemble learning; feature extraction; Skin cancer detection
Dermatograms are pivotal in the early detection of skin cancer, a disease with significant mortality rates. This paper introduces a novel feature extraction method that captures irregularities in the boundaries of abnormal skin regions. Each raw dermatogram is converted into a binary mask image using an effective segmentation algorithm. The boundary of the lesion region is extracted from the mask. The boundary, together with the centroid of the lesion mask, is used to define a set of directional vectors. An Arc is defined using these directional vectors, and a new Arc feature is calculated based on the number of times the lesion boundary crosses the arc. The proposed Arc feature is evaluated using three standard skin lesion datasets: ISBI 2016, HAM10000, and PH2. Additionally, color features and Local Binary Pattern (LBP) features are implemented for comparison. Classical machine learning algorithms are employed to evaluate these features. Results indicate that for the ISBI 2016 and HAM10000 datasets, the Arc feature set demonstrates superior classification accuracy. In contrast, the PH2 dataset benefits more from the LBP feature. Comparative analysis with recent studies highlights the dependency of accuracy on datasets and classifiers, underscoring the necessity for models incorporating feature fusion and ensemble classifiers. The proposed method outperforms traditional color and texture features and shows competitive results against deep learning models, particularly in scenarios with limited computational resources. These findings suggest that the Arc feature is a promising approach for improving skin cancer detection, although further investigation is needed to fine-Tune performance, optimize classifier selection, and explore feature fusion strategies. 2024 World Scientific Publishing Company.
Raju G.; Nikesh P.; Resmi K.R.; Swain D.; Acharya B.; Gerogiannis V.C.; Kanavos A.
International Journal on Artificial Intelligence Tools
Raju G., Department of Computer Science and Engineering, Christ University, Bengaluru, Karnataka, India; Nikesh P., Department of Computer Science and Engineering, Government Engineering College, Wayanad, Kerala, India; Resmi K.R., Department of Computer Science and Engineering, Christ University, Bengaluru, Karnataka, India; Swain D., Department of Computer Science and Engineering, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India; Acharya B., Department of Computer Engineering-AI, Marwadi University, Gujarat, Rajkot, India; Gerogiannis V.C., Department of Digital Systems, University of Thessaly, Larissa, Greece; Kanavos A., Department of Informatics, Ionian University, Corfu, Greece
Advanced Machine Vision Paradigms for Medical Image Analysis
Computer vision and machine intelligence paradigms are prominent in the domain of medical image applications, including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics. Medical image analysis and understanding are daunting tasks owing to the massive influx of multi-modal medical image data generated during routine clinal practice. Advanced computer vision and machine intelligence approaches have been employed in recent years in the field of image processing and computer vision. However, due to the unstructured nature of medical imaging data and the volume of data produced during routine clinical processes, the applicability of these meta-heuristic algorithms remains to be investigated. Advanced Machine Vision Paradigms for Medical Image Analysis presents an overview of how medical imaging data can be analyzed to provide better diagnosis and treatment of disease. Computer vision techniques can explore texture, shape, contour and prior knowledge along with contextual information, from image sequence and 3D/4D information which helps with better human understanding. Many powerful tools have been developed through image segmentation, machine learning, pattern classification, tracking, and reconstruction to surface much needed quantitative information not easily available through the analysis of trained human specialists. The aim of the book is for medical imaging professionals to acquire and interpret the data, and for computer vision professionals to learn how to provide enhanced medical information by using computer vision techniques. The ultimate objective is to benefit patients without adding to already high healthcare costs. 2020 Elsevier Inc. All rights reserved.
Gandhi T.; Bhattacharyya S.; De S.; Konar D.; Dey S.
Advanced Machine Vision Paradigms for Medical Image Analysis, pp. 1-289.
Gandhi T., Department of Electrical Engineering, Indian Institute of Technology, Delhi, New Delhi, India; Bhattacharyya S., Christ University, Department of Computer Science and Engineering, Karnataka, Bangalore, India; De S., Cooch Behar Government Engineering College, Department of Computer Science and Engineering, West Bengal, Cooch Behar, India; Konar D., Sikkim Manipal Institute of Technology, Department of Computer Science and Engineering, Sikkim Manipal University, Sikkim, Majitar, India; Dey S., Sukanta Mahavidyalaya, Department of Computer Science, West Bengal, Dhupguri, Jalpaiguri, India
Advanced Materials for Next-Generation Energy Storage Devices: A Focus on Efficiency and Cost Reduction
Advanced materials; cost reduction; efficiency optimization; energy storage; nanomaterials
The increasing demand for efficient and cost-effective energy storage systems has pushed extensive research into improved materials for next-generation energy storage devices. This study discusses the crucial significance of material advances in boosting the performance and reducing the costs of storage technologies such as batteries and supercapacitors. Conventional energy storage systems face limits in energy density, charge or discharge rates, and scalability, which impede their broad implementation. Advanced materials, including nanomaterials, solid-state electrolytes, and innovative electrode compounds, offer solutions to these difficulties by enhancing energy efficiency, power output, and overall longevity. Additionally, the use of plentiful and low-cost materials, such as sodium-ion and aluminium-based compounds, presents prospects for significant cost savings. This research analyzes current trends, issues in material manufacturing, and future perspectives for energy storage systems, concentrating on balancing efficiency improvements with cost-effectiveness to enable the rising integration of renewable energy sources. The development of these materials is important to creating sustainable, scalable, and economical energy storage systems for the future. The Authors, published by EDP Sciences.
Chandrakala P.; Liz A.S.; Mane V.; Jeshurun S.B.; Sharma G.; Pardeshi P.R.; Senthil kumar R.
https://doi.org/10.1051/e3sconf/202459101001 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211801054&doi=10.1051%2fe3sconf%2f202459101001&partnerID=40&md5=6a6055616267c09ddd036f1ce952ef45
Chandrakala P., Department of EEE, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, 127, India; Liz A.S., Department of CSE, New Prince Shri Bhavani College of Engineering and Technology, Tamil nadu, Chennai, 600073, India; Mane V., Vishwakarma Institute of Technology, Pune, India; Jeshurun S.B., School of Business and Management, CHRIST University, Bangaluru, India; Sharma G., Institute of Business Management, GLA University, Mathura, India; Pardeshi P.R., Department of Mechanical Engineering, Dr. D. Y. Patil Institute of Techology, Pimrpi, Pune, India; Senthil kumar R., AAA College of Engineering and Technology, Sivakasi, India
Advanced Materials from Biomass and Its Role in Carbon-Di-Oxide Capture
Biomass; Carbon sequestration; CO2; Nanotechnology; Pyrolysis
This chapter explores utilizing agricultural waste for developing advanced materials for CO2 capture, overcoming drawbacks of conventional adsorbents. It compares biomass-based activated carbons CO2 adsorption capabilities to commercial adsorbents, highlighting promising performance. Strategies for enhancing selectivity and efficiency through functional group hybridization are discussed, alongside investigations into operational parameters effects on material properties and CO2 uptake. Additionally, the chapter reviews biomass-derived carbon materials role in CO2 capture, detailing conversion techniques like pyrolysis and hydrothermal carbonization. Various modification methods, including activation and N-doping, are examined for enhancing CO2 capture. Discussion extends to diverse advanced materials derived from biomass, including biochar and activated carbon. The chapter underscores the circular-economy impact of utilizing biomass-derived porous carbons in CO2 capture processes, particularly in biogas upgrading to biomethane. Overall, it offers insights into addressing CO2 capture challenges, proposing future research directions in this field. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Materials Horizons: From Nature to Nanomaterials, Vol-Part F3306, pp. 237-247.
Nair J.B., Serah Zines LLP, C/O Seed Foundation IISER Pune, Maharashtra, Pune, 411008, India; Joseph A.M., Department of Chemistry, Newman College Thodupuzha, Kerala, Thodupuzha, 685585, India; Joseph M.M., Department of Life Sciences, School of Science, CHRIST University, Karnataka, Bengaluru, 560029, India
Advanced Sentiment Analysis: From Lexicon-Enhanced BERT to Dimensionality Reduction Using NLP
BERT Embeddings; Lexicon-Based Sentiment Analysis; Natural Language Processing (NLP); Sentiment Analysis in Many Different Languages
Social media platforms serve as vital connections for communication, generating massive quantities of data that represent an array of perspectives. Efficient sentiment analysis is necessary for understanding public opinion, particularly in domains such as product reviews and socio-political discussion. This paper develops a novel sentiment analysis model that is customized for social media data by integrating machine learning algorithms, language processing techniques with part-of-speech tagging, and dimensionality reduction methods. The model will improve sentiment analysis performance by tackling challenges like noise and data domain variations. To further improve sentiment representation, it includes convolutional neural networks (CNNs), BERT embeddings, N-grams, and sentiment lexicons. The model's effectiveness is determined on a variety of datasets, which enhances sentiment analysis in social media discussion. This paper goes beyond sentiment analysis in code-mixed, multilingual text and highlights the importance of careful data before treatment and an extensive variety of ML algorithms. This study attempts to explain the nuances of sentiment analysis and its use in social media discussions through methodical research. 2024 IEEE.
Kumar V.; Chauhan P.S.; Lella J.A.; Momidi M.; Gupta P.; Upreti K.
2024 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024 - Proceedings, pp. 441-446.
Kumar V., SEAS, GWU, Washington, DC, United States; Chauhan P.S., Global Payments, Atlanta, United States; Lella J.A., Electrical & Comm. Engg., National Institute of Technology, Calicut, India; Momidi M., Dell Technologies, Bangalore, India; Gupta P., Computer Science Engineering, ADGIPS, Delhi, India; Upreti K., Christ University, Dept. of Computer Science, Delhi NCR Campus, India
Advanced Technological Improvements in Making of Smart Production Using AI and ML
Applications; Artificial Intelligence; Industry 4.0; Machine Learning; Smart Production; Sustainability; Systematic Literature Review
The necessity for adaptation and creativity in the manufacturing sector demonstrates the importance of sustainable manufacturing by the merging of advanced technologies. To encourage sustainability, a global view on the integration of smart manufacturing procedures is important. Artificial intelligence (or AI) has appeared as a crucial factor in achieving environmentally conscious manufacturing, with methods like the use of machine learning (ML) getting popularity. This study carefully studies the scientific papers related to the usage of AI and ML in business. The emergence of Industry 4.0 as a whole has positioned machine learning (ML) and artificial intelligence (AI) as drivers for the smart industry change. The study categorizes material based on release year, writers, scientific field, country, institution, and terms, applying the Web of Biology and SCOPUS databases. Utilize UCINET alongside NVivo 12 software, thereby the analysis covers empirical studies on machine learning (ML) and artificial intelligence (AI) via 1999 until the present, showing their growth before and after the start of Industry 4.0. Notably, the USA displays a substantial addition to this area, with a noticeable surge in desire following the rise of Industry 4.0. 2024 IEEE.
Praveenraj D.D.W.; Vijayaraj T.; Kumar K.S.; Sakthivel N.K.; Multani F.; Nagaveni P.
2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 178-183.
https://doi.org/10.1109/ICACITE60783.2024.10617114 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201804747&doi=10.1109%2fICACITE60783.2024.10617114&partnerID=40&md5=5bda97679cfbd0b5cc549dba6fe853f8
Praveenraj D.D.W., Christ University, School of Business and Management, TN, Bangalore, India; Vijayaraj T., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Mechanical Engineering, TN, India; Kumar K.S., Department of Electronics and Communication Engineering, Chennai, Tamil Nadu, Avadi, India; Sakthivel N.K., Nehru Institute of Engineering and Technology, Department of Computer Science and Engineering, Tamil Nadu, T.M. Palayam, India; Multani F., Ies College of Technology, Department of Computer Science& Engineering, M.P, Bhopal, India; Nagaveni P., Karpagam Academy of Higher Education, Department of Electrical and Electronics Engineering, TN, India
Advancement and Challenges of Biosensing Using Field Effect Transistors
biosensors; Debye length; field effect transistors; functionalization; screening; sensitivity; specificity
Field-effect transistors (FETs) have become eminent electronic devices for biosensing applications owing to their high sensitivity, faster response and availability of advanced fabrication techniques for their production. The device physics of this sensor is now well understood due to the emergence of several numerical modelling and simulation papers over the years. The pace of advancement along with the knowhow of theoretical concepts proved to be highly effective in detecting deadly pathogens, especially the SARS-CoV-2 spike protein of the coronavirus with the onset of the (coronavirus disease of 2019) COVID-19 pandemic. However, the advancement in the sensing system is also accompanied by various hurdles that degrade the performance. In this review, we have explored all these challenges and how these are tackled with innovative approaches, techniques and device modifications that have also raised the detection sensitivity and specificity. The functional materials of the device are also structurally modified towards improving the surface area and minimizing power dissipation for developing miniaturized microarrays applicable in ultra large scale integration (ULSI) technology. Several theoretical models and simulations have also been carried out in this domain which have given a deeper insight on the electron transport mechanism in these devices and provided the direction for optimizing performance. 2022 by the authors.
All Open Access; Gold Open Access; Green Open Access
ISSN: 20796374; PubMed ID: 36005043; CODEN: BISSE
https://doi.org/10.3390/bios12080647 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137127533&doi=10.3390%2fbios12080647&partnerID=40&md5=5e69c15cc53200417800b8335ce06662
Thriveni G., Department of Electronics and Communication Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Mysore Road, Kumbalgodu, Bengaluru, 560074, India; Ghosh K., Centre for Nanoelectronics and VLSI Design, Vellore Institute of Technology, Vandalur Kelambakkam Road, Chennai, 600127, India, Vellore Institute of Technology, School of Electronics Engineering, Vandalur Kelambakkam Road, Chennai, 600119, India