302 20 16481 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource K.S., Shanthini; George, Sudhish N.; Francis, Jobin; George, Sony Title A name given to the resource SS-CNN BruiseFinder: Hyperspectral imaging and CNN-driven spatial-spectral fusion for non-destructive plum bruise analysis Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Food Control;Volume;182;Issue;;Article No.;111870; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.foodcont.2025.111870" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.foodcont.2025.111870</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022807709?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022807709?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant K.S. S., Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kerala, India; George S.N., Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kerala, India; Francis J., Department of Computer Science, Christ University Bangalore, Karnataka, India; George S., Department of Computer Science, Norwegian University of Science and Technology Gjik, Norway Description An account of the resource Plum fruit is susceptible to damage at various stages, from growth to packaging, and such bruising is often difficult to detect visually due to its subtle surface appearance. This research seeks to develop a convolutional neural network (CNN) model that leverages 3D convolutional layers to integrate spatial and spectral features from hyperspectral data, enabling accurate bruise analysis in plum fruit. In this study, plums sourced from a Norwegian fruit store were intentionally bruised and then imaged using hyperspectral technology at various time intervals (30 min to 48 h post-bruising). A novel CNN model, dubbed SS-CNN BruiseFinder, is developed to harness the spatial and spectral characteristics of these hyperspectral images for accurate bruise detection and classification. The SS-CNN BruiseFinder model demonstrates detection accuracy ranging from 68.5% to 91.5% and categorization accuracy between 67.39% and 98.16%. To further establish the effectiveness of this approach, three additional deep learning models a custom spectral CNN, ResNet 101, and a bidirectional LSTM model are developed and evaluated on the same dataset, providing a comprehensive validation of the proposed method's superiority. Timely detection of bruising helps prevent contaminated plums from entering the supply chain during transportation or storage. By categorizing plums based on bruise age, retailers can offer consumers more accurate freshness and quality information, enabling them to make better-informed purchasing choices and ultimately enhancing the overall shopping experience. To encourage community engagement and re-implementation, our code is available at https://github.com/SS-CNN BruiseFinder. 2025 Elsevier Ltd Subject The topic of the resource Bruise categorization; Fruit quality analysis; Postharvest; Spatial spectral CNN; Vis-NIR imaging Publisher An entity responsible for making the resource available Elsevier Ltd Relation A related resource ISSN: 9567135; CODEN: FOOCE Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource K.S., Shanthini; George, Sudhish N.; O.V., Athul Chandran; K.M., Jinumol; P., Keerthana; Francis, Jobin; George, Sony Title A name given to the resource NorBlueNet: Hyperspectral imaging-based hybrid CNN-transformer model for non-destructive SSC analysis in Norwegian wild blueberries Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Computers and Electronics in Agriculture;Volume;235;Issue;;Article No.;110340; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.compag.2025.110340" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.compag.2025.110340</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105002221359?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002221359?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant K.S. S., Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kerala, India; George S.N., Department of Electronics and Communication Engineering, National Institute of Technology Calicut, Kerala, India; O.V. A.C., Department of Applied Electronics and Instrumentation Engineering, Government Engineering College Kozhikode, Kerala, India; K.M. J., Department of Applied Electronics and Instrumentation Engineering, Government Engineering College Kozhikode, Kerala, India; P. K., Department of Applied Electronics and Instrumentation Engineering, Government Engineering College Kozhikode, Kerala, India; Francis J., Department of Computer Science, Christ University Bangalore, Karnataka, India; George S., Department of Computer Science, Norwegian University of Science and Technology Gjik, Norway Description An account of the resource Soluble solids content (SSC) is a vital parameter in blueberries, reflecting the concentration of dissolved sugars (primarily fructose and glucose) and directly influencing the fruit's sweetness, flavour, and ripeness. As part of this study, Norwegian wild blueberries were carefully hand-picked from a forest in Norway and subsequently imaged using a hyperspectral camera to capture their detailed spectral characteristics. This study introduces NorBlueNet, a hybrid CNN-transformer architecture, for accurately predicting SSC in wild blueberries through hyperspectral imaging and deep learning. This hybrid architecture combines CNN layers for local feature extraction and spatial hierarchy representation, followed by transformer layers that capture global relationships and long-range dependencies. The hybrid approach combines the computational advantages of CNNs with the advanced attention mechanisms of transformers, achieving enhanced accuracy while maintaining computational efficiency. A comprehensive evaluation is conducted by comparing the proposed model with two additional deep learning models on the custom dataset. The results indicate that the NorBlueNet achieves the highest prediction accuracy, with an R2 = 0.98, RMSE = 0.0136, and RPD = 9.3759 thereby demonstrating its superior performance. To foster community engagement, collaboration and facilitate re-implementation of our work, we have made our code available at:https://github.com/NorBlueNet. 2025 Subject The topic of the resource Fruit quality analysis; Hyperspectral images; Non-destructive analysis; SSC prediction model Publisher An entity responsible for making the resource available Elsevier B.V. Relation A related resource ISSN: 1681699; CODEN: CEAGE Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Reviews Review Faculty Publications- Reviews Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Polymer-nanocarbon composites: a promising strategy for enhanced performance of organic solar cells Subject The topic of the resource Conducting/conjugated polymers; Conjugated polymer-nanocarbon composites; Nanocarbon materials; Polymer solar cells Description An account of the resource The exigency for sustainable and clean energy resources has led to profound research in development of various generations of solar cells, aiming to control the over-exploitation of fossil fuels and subsequently limit environmental degradation. Among the fast-emerging third-generation solar cells, polymer solar cell technology has gained much consideration due to its potential for achieving economically feasible, lightweight, flexible solar energy harvesting devices. As a predominant research area, at present, the major concerns regarding polymer solar cells include improving conversion efficiency, enhancing absorption bandgap in polymers, limiting photochemical degradation, and remediating low dielectric constant. Nanocarbon materials can be effectively blended with polymers and have been widely reported to enhance the performance of polymer solar cells owing to their desirable characteristics like high electrical conductivity, mechanical strength, thermal stability, non-toxicity, large specific surface area, flexibility, and optical transparency. In this review, we briefly discuss various conjugated polymer-nanocarbon composites, including polymer/graphene derivatives, polymer/graphene quantum dots (GQD), and polymer/carbon nanotubes (CNTs), elucidating their roles in the performance enhancement of polymer solar cells (PSCs). Graphical abstract: (Figure presented.). The Author(s) 2023. Creator An entity primarily responsible for making the resource K.V G.M.; George J.; Balachandran M. Source A related resource from which the described resource is derived Emergent Materials, Vol-7, No. 1, pp. 17-33. Publisher An entity responsible for making the resource available Institute for Ionics Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s42247-023-00604-x" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42247-023-00604-x</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180504917&amp;doi=10.1007%2Fs42247-023-00604-x&amp;partnerID=40&amp;md5=becb90bc5bcf54794a4778062e97e033" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180504917&amp;doi=10.1007%2fs42247-023-00604-x&amp;partnerID=40&amp;md5=becb90bc5bcf54794a4778062e97e033</a> Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Relation A related resource ISSN: 25225731 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Review Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant K.V G.M., Department of Physics and Electronics, Christ (Deemed to Be University), Bangalore, 560029, India, Department of Physics, Christ Academy Institute for Advanced Studies, Bangalore, 560083, India; George J., Department of Physics and Electronics, Christ (Deemed to Be University), Bangalore, 560029, India; Balachandran M., Department of Physics and Electronics, Christ (Deemed to Be University), Bangalore, 560029, India https://archives.christuniversity.in/files/original/36b89ec057dfaa5b034d1f94c48b052c.pdf 7b7067a1071d7314b6caf7d712326065 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource MPHIL Mphil Mphil Thesis Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Impact of Intervention of PERMA Model on Subjective Well-Being among Couple Creator An entity primarily responsible for making the resource K.V. Rosy Date A point or period of time associated with an event in the lifecycle of the resource 2018 Source A related resource from which the described resource is derived Psychology Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource K.V., Thomas; Birari, Abhijeet; Rattan, Punam Title A name given to the resource Multimodal data generation and synthesis Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Multimodal Learning Using Heterogeneous Data;pp.117-137 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-27528-9.00018-8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-27528-9.00018-8</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032986083?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032986083?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant K.V. T., Department of Commerce, CHRIST (Deemed to be University), Lavasa Campus, Maharashtra, Pune, India; Birari A., Department of Commerce, CHRIST (Deemed to be University), Lavasa Campus, Maharashtra, Pune, India; Rattan P., School of Computer Application, Lovely Professional University, Punjab, Phagwara, India Description An account of the resource Multimodal data generation and synthesis have become new promising directions in artificial intelligence research, making possible the combination and transformation of the different data modalities: text, images, audio, and video. In this chapter a look will be made about the principles, methodologies, applications, and challenges linked with multimodal data, bringing attention to the current trends and needs regarding multimodal systems and systems approaches to tackle complex real-world challenges across the medical and health care, autonomous systems, entertainment, and extended reality (XR) fields. The chapter introduces multimodal data and discusses how the approach differs from unimodal methods, considering the merits of working with multiple data forms. Multimodal systems present richer and more comprehensive representations that lead to better decision-making and provide a better interaction with users. The complexity due to alignment, synchronization, and representation of diverse modes is inherently difficult. This section further discusses state-of-the-art techniques in multimodal synthesis, especially focusing on generative approaches like generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models. These methods are shown to facilitate cross-modal transformations, such as text-to-image or audio-to-video synthesis, driving innovation in artificial intelligence and beyond. Applications of multimodal data synthesis are discussed in detail, underscoring its transformative impact. In health care, for instance, synthesizing medical images paired with textual annotations enhances diagnostic accuracy and medical training. Autonomous vehicles benefit from the integration of LiDAR, visual, and auditory data, enabling robust decision-making in real-time environments. Similarly, in entertainment and XR, multimodal synthesis is redefining content creation, making immersive experiences more personalized and dynamic. The chapter also delves into novel applications such as multimodal translation, exemplified by systems that translate sign language into spoken text, fostering inclusivity and accessibility. Despite its potential, multimodal synthesis faces critical challenges, including bias in data and models, privacy concerns, and the ethical implications of creating hyperrealistic synthetic data, such as deepfakes. All these raise pressing concerns, and addressing these requires robust privacy-preserving techniques, bias-mitigation strategies, and stringent ethical guidelines. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource artificial intelligence; autonomous systems; bias mitigation; cross-modal generation; data representation; data synthesis; deep learning; edge computing; ethical AI; extended reality (XR); generative adversarial networks (GANs); healthcare applications; immersive experiences; Multimodal data; multimodal translation; privacy concerns; quantum computing; text-to-image synthesis; variational autoencoders (VAEs) Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044327528-9; 978-044327529-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource K.V., Thomas; Rattan, Punam; Nair, Raji Ramakrishnan Title A name given to the resource An introduction to multimodal data representation Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Multimodal Learning Using Heterogeneous Data;pp.17-30 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-27528-9.00003-6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-27528-9.00003-6</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032985231?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032985231?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant K.V. T., Department of Commerce, CHRIST (Deemed to be University), Lavasa Campus, Maharashtra, Pune, India; Rattan P., School of Computer Application, Lovely Professional University, Punjab, Phagwara, India; Nair R.R., School of Business and Management (MBA), CHRIST (Deemed to be University), Lavasa Campus, Maharashtra, Pune, India Description An account of the resource The contemporary digital epoch is characterized by a radical transformation of data representation methodologies that imply increased intricacy as well as an enlarged bulk of data. An unimodal approach focusing on judicious data types, considered in isolation, was the earlier norm. The emphasis was on structured data, which had the advantage of being arranged systematically within relational databases and entity-relationship frameworks. This facilitated efficient data management. With the introduction of the internet and digital communication, such unstructured data as textual content, images, and audio began to be placed up front. But unimodal techniques were not adequately equipped to manage the intricate and interconnected nature of real-world phenomena. The welcome result was the development of multimodal data representation methodologies, which constitute a sophisticated paradigm that integrates data from such varied sources as text, images, audio, video, and sensor data. This results in a more holistic comprehension of complex scenarios. Distinct attributes and inherent challenges characterize each modality. To exemplify, text data need advanced natural language processing strategies to comprehend context and semantics; Image data necessitate methodologies well versed in managing spatial features and elevated dimensionality; audio data requires concentration on temporal patterns and noise; video data, on the contrary, integrates these complexities, leading to efficient processing techniques to accommodate its substantial volume and dynamic characteristics. The unsynchronous and heterogeneous sensor data complicate the integration of diverse data streams. Sophisticated fusion techniques, that is, early fusion, late fusion, and hybrid fusion, capable of integrating features from various modalities, are employed to mitigate the challenges faced by multimodal data representation. It increases interpretative insights and precision. The deep learning technologies, such as convolutional neural networks for image analysis, recurrent neural networks for sequential data processing, and attention mechanisms, have led to advancements in this domain. These models have become competent in recognizing complex patterns across modalities. Naturally, they bring about significant progress in domains such as health care, autonomous systems, multimedia processing, and natural language comprehension. This chapter explores the historical background of data representation, right from the beginnings in unimodal to its advancement in multimodal. The unique characteristics and challenges associated with each modality are scrutinized; Fusion techniques alongside contemporary deep learning models are examined; and underscore real-world applications, which are effective examples of the transformative potential of multimodal data representation. The chapter also emphasizes the necessity of escalating these methodologies in an increasingly data-centric world. It lays the foundation for advancements in the future with the goal of overcoming existing limitations and enlarging the scope of multimodal applications. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource audio recognition; convolutional neural networks (CNNs); data complexity; data fusion; data integration; deep learning; early fusion; feature extraction; hybrid fusion; image processing; late fusion; Multimodal data representation; natural language processing (NLP); recurrent neural networks (RNNs); sensor data; structured data; text analysis; unimodal techniques; unstructured data; video analysis Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044327528-9; 978-044327529-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kachroo, Nitya Title A name given to the resource Breaking the Glass Ceiling: Will the Role of Organizational Workplace Policies Perpetuate or Mitigate Gender Bias? Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Bridging the Leadership Gap: Policies and Workplace Equality;pp.37-70 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-6345-5.ch002" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-6345-5.ch002</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018098497?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018098497?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kachroo N., Christ University, India Description An account of the resource Despite significant global progress in narrowing gender gaps, inequality persists across many countries. Organizations like the Global Gender Gap Index and the European Institute for Gender Equality monitor improvements in political leadership, economic opportunities, health, and education. However, women continue to face challenges, including unequal pay, limited career advancement, and imbalanced household labor. The "glass ceiling" refers to invisible barriers that prevent women from achieving top positions despite equal qualifications. Long-term effects include temporary employment and lower retirement savings. True gender equality requires more than quotas-it demands equitable opportunities, flexible work policies, pay transparency, and mentorship programs. Tackling unconscious bias and fostering inclusive environments is essential for sustainable change and women's holistic success. 2026, IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-833736347-9; 979-833736345-5; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Compact Super Wideband Antenna with Controllable Dual Notch Band Capability Subject The topic of the resource Band notch; Bandwidth dimension ratio; Planar monopole; Super wideband antenna Description An account of the resource In this paper, a novel super wideband (SWB) antenna with dual band notch capability is designed and analyzed for wide band applications. The proposed antenna consists of a pentagonal shaped radiator, beveled-shaped partial ground plane with slot and U-shaped parasitic strips. The beveled-shaped defected ground structure with rectangular slot helps to realize wideband characteristics from 2.4 to 28.2 GHz. Independent control of the notch band's center frequency and bandwidth is achieved by using U-shaped parasitic strips. This key feature is achieved in the WiMAX (3.3 to 3.7 GHz) and WLAN (5.1 to 5.9 GHz) bands. Furthermore, it exhibits a stable radiation pattern and offers acceptable gain over the entire operating bandwidth with sharp decrease in gain at the notches. The percentage bandwidth of 169% is achieved with a bandwidth dimension ratio (BDR) of 6986. Group delay is less than 1 ns in the entire operating bandwidth except at the notch bands. The measured reflection and radiation characteristics of fabricated SWB antenna are in good agreement with the simulation results. The proposed antenna has the advantage of simple design and compact size with an overall dimension of 18 x 21 x 1.6 mm3. The performance of the proposed antenna is superior compared to reported antenna designs in terms of controllable sharp notches and size for the bandwidth achieved. 2022 IAMOT Creator An entity primarily responsible for making the resource Kadam B.V.; Gudino L.J.; Ramesh C.K.; Rodrigues J.X. Source A related resource from which the described resource is derived International Journal of Microwave and Optical Technology, Vol-17, No. 6, pp. 592-603. Publisher An entity responsible for making the resource available International Academy of Microwave and Optical Technology (IAMOT) Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141787717&amp;partnerID=40&amp;md5=0bf3f3a5b8ca24937f75015ee562cbea" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141787717&amp;partnerID=40&amp;md5=0bf3f3a5b8ca24937f75015ee562cbea</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 15530396 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kadam B.V., Department of Electrical and Electronics Engineering, BITS Pillai, K. K. Birla Goa Campus Goa, India; Gudino L.J., Department of Computer Science and Information Systems, BITS Pillai, Pillai, India; Ramesh C.K., Department of Electrical and Electronics Engineering, BITS Pillai, K. K. Birla Goa Campus Goa, India; Rodrigues J.X., Department of Electrical and Electronics Engineering, Christ University, Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Single Port Multimode Reconfigurable UWB-NB Antenna for Cognitive Radio Applications Subject The topic of the resource Cognitive radio; Feeding network; Monopole; Reconfigurable antenna; Ultrawideband (UWB) Description An account of the resource In this paper, a compact, single port, multimode reconfigurable UWB-NB antenna with a novel feeding network is presented. The proposed antenna consists of a pentagonal-shaped monopole radiator, a beveled-shaped partial defected ground plane with a rectangular slot, and a reconfigurable bypass feeding network. The antenna realizes a wideband frequency range from 2.4 to 18 GHz and four narrow band frequency ranges, 5.3 to 6.8 GHz, 6.0 to 7.6 GHz, 7.2 to 8.8 GHz and 8.4 to 11.4 GHz. The antenna provides an omnidirectional radiation pattern with gain from 2.2 to 6.2 dBi maximum at 12 GHz and voltage standing wave ratio (VSWR) ranges from 1 to 2. The fabricated antenna has an overall dimension of 181.6 mm3. Sensing and tuning ranges of the fabricated antenna shows good agreement with the simulation results. The proposed antenna has an advantage of simple design, low profile, single port excitation and omnidirectional radiation pattern making it suitable for applications such as handheld mobile cognitive radio systems. 2022 SBMO/SBMag Creator An entity primarily responsible for making the resource Kadam B.V.; Gudino L.J.; Ramesha C.K.; Rodrigues J.X. Source A related resource from which the described resource is derived Cardiovascular Therapy and Prevention (Russian Federation), Vol-21, No. 4, pp. 623-638. Publisher An entity responsible for making the resource available Silicea-Poligraf Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1590/2179-10742022v21i4264212" target="_blank" rel="noreferrer noopener">https://doi.org/10.1590/2179-10742022v21i4264212</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146530536&amp;doi=10.1590%2F2179-10742022v21i4264212&amp;partnerID=40&amp;md5=1f3379dbda27aaa7f59efe0e645bc0bc" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146530536&amp;doi=10.1590%2f2179-10742022v21i4264212&amp;partnerID=40&amp;md5=1f3379dbda27aaa7f59efe0e645bc0bc</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 17288800 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kadam B.V., Department of Electrical and Electronics Engineering, BITS Pilani, K. K. Birla Goa Campus, Goa, India; Gudino L.J., Department of Computer Science and Information Systems, BITS Pilani, Pilani, India; Ramesha C.K., Department of Electrical and Electronics Engineering, BITS Pilani, K. K. Birla Goa Campus, Goa, India; Rodrigues J.X., Department of Electrical and Electronics Engineering, Christ University, Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Relationships between Ultrasonographic Placental Thickness in the Third Trimester and Foetal Outcomes Subject The topic of the resource Gestational diabetes; Gestational Diabetes Mellitus; Oligohydramnios; Preeclampsia; Ultrasonographic Description An account of the resource Poor neonatal outcomes, including low birth weight (LBW), poor APGAR scores, more NICU hospitalizations, and a higher chance to develop Pre-Eclampsia, IUGR, and Oligo Hydramnios, are all linked to thin placental thickness. While both thin and thick placentae are connected to a greater prevalence of C-sections, thick placentae are linked with a greater possibility of developing GDM and an increase in NICU hospitalizations. Objective of this research was to investigate the association between placental thickness as measured by ultrasonography in the third trimester and foetal outcome, including the relationship between placental histopathology and placental thickness. investigate the link among placental thickness, foetal outcome, and placental histology. Most newborns had fibrinoid necrosis and calcifications. Babies with Macrosomia and IUGR, respectively, were more likely to develop Syncytial knots and thickening of the vessel wall. Patients with normal placenta thickness at 36 weeks' gestation experienced fewer difficulties than those with thin or thick placentas at the same time. The study emphasizes the value of evaluating placental thickness using ultrasound in the third trimester to detect high-risk pregnancies. The study also shows that aberrant foetal and neonatal events are linked to certain placental histological characteristics, like artery wall thickening and infarctions. RJPT All right reserved. Creator An entity primarily responsible for making the resource Kadam D.; Patil Y.; Patange R.P.; Patil S.; Sebastian S.; Jarag R. Source A related resource from which the described resource is derived Research Journal of Pharmacy and Technology, Vol-17, No. 2, pp. 746-750. Publisher An entity responsible for making the resource available Research Journal of Pharmacy and Technology Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.52711/0974-360X.2024.00116" target="_blank" rel="noreferrer noopener">https://doi.org/10.52711/0974-360X.2024.00116</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189954318&amp;doi=10.52711%2F0974-360X.2024.00116&amp;partnerID=40&amp;md5=899e4197df2119c724f5fc3fc02a9c16" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189954318&amp;doi=10.52711%2f0974-360X.2024.00116&amp;partnerID=40&amp;md5=899e4197df2119c724f5fc3fc02a9c16</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 9743618 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kadam D., Department of Gynecology, Krishna Institute of Medical Sciences, Krishna Vishwa Vidyapeeth Deemed to be University, Maharashtra, Karad, India; Patil Y., Department of Gynecology, Krishna Institute of Medical Sciences, Krishna Vishwa Vidyapeeth Deemed to be University, Maharashtra, Karad, India; Patange R.P., Department of Gynecology, Krishna Institute of Medical Sciences, Krishna Vishwa Vidyapeeth Deemed to be University, Maharashtra, Karad, India; Patil S., Department of Gynecology, Krishna Institute of Medical Sciences, Krishna Vishwa Vidyapeeth Deemed to be University, Maharashtra, Karad, India; Sebastian S., Christ (Deemed to be University), India; Jarag R., Bharati Vidyapeeth College of Pharmacy, Maharashtra, Kolhapur, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Predicting Crude Oil Future Price Using Traditional and Artificial Intelligence-Based Model: Comparative Analysis Subject The topic of the resource ALSTM; ARIMA; Artificial intelligence; crude oil; forecast; RNN-LSTM Description An account of the resource Crude oil is an imperative energy source for the global economy. The future value of crude oil is challenging to anticipate due to its nonstationarity in nature. The focus of this research is to appraise the explosive behavior of crude oil during 20072022, including the most recent influential crisis COVID-19 pandemic, to forecast its prices. The crude oil price forecasts by the traditional econometric ARIMA model were compared with modern Artificial Intelligence (AI)based Long Short-Term Memory Networks (ALSTM). Root mean square error (RMSE) and mean average percent error (MAPE) values have been used to evaluate the accuracy of such approaches. The results showed that the ALSTM model performs better than the traditional econometric ARIMA forecast model while predicting crude oil opening price on the next working day. Crude oil investors can effectively use this as an intraday trading model and more accurately predict the next working day opening price. 2023 World Scientific Publishing Co. Pte Ltd. All rights reserved. Creator An entity primarily responsible for making the resource Kadam S.; Agrawal A.; Bajaj A.; Agarwal R.; Kalra R.; Shah J. Source A related resource from which the described resource is derived Journal of International Commerce, Economics and Policy, Vol-14, No. 3 Publisher An entity responsible for making the resource available World Scientific Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1142/S179399332350014X" target="_blank" rel="noreferrer noopener">https://doi.org/10.1142/S179399332350014X</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85158874498&amp;doi=10.1142%2FS179399332350014X&amp;partnerID=40&amp;md5=1f8248dcbc3b6320d6e0469973b0c2be" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85158874498&amp;doi=10.1142%2fS179399332350014X&amp;partnerID=40&amp;md5=1f8248dcbc3b6320d6e0469973b0c2be</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 17939933 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kadam S., Symbiosis Institute of Business Management Pune Symbiosis International (Deemed University), Maharashtra, India; Agrawal A., Jaypee Institute of Information Technology, Noida, India; Bajaj A., GISMA School of Business University of Applied Sciences, Potsdam, Germany; Agarwal R., University School of Business Chandigarh University, Punjab, Mohali, 140413, India; Kalra R., School of Business Management CHRIST (Deemed to be University), Bangalore, India; Shah J., Amity Business School Amity University, Panvel, Mumbai, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kadam, Meghan; Deshpande, Madhumati; Balakrishnan, C.; Ilankumaran, G.; Kunnumpurath, Biju; Thomas, Joby; Paul, Ajith; Suseelan, Anitha; Prakash, Vishnu P. Title A name given to the resource Colonial Migration and Cultural Transformation in India and Burma: Exploring the Role of Transnational Mobility in Shaping Chettinad Heritage Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Studies in Ethnicity and Nationalism; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1111/sena.70053" target="_blank" rel="noreferrer noopener">https://doi.org/10.1111/sena.70053</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105039480840?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039480840?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kadam M., Department of International Studies, Political Science and History, Christ University, Bengaluru, India; Deshpande M., Department of International Studies, Political Science and History, Christ University, Bengaluru, India; Balakrishnan C., Department of Computer Science, Christ University, Bengaluru, India; Ilankumaran G., Alagappa Institute of Management, Alagappa University, Karaikudi, India; Kunnumpurath B., Department of Media Studies, Christ University, Bengaluru, India; Thomas J., School of Business and Management, Christ University, Bengaluru, India; Paul A., Department of Media Studies, Christ University, Bengaluru, India; Suseelan A., School of Architecture, Christ University, Bengaluru, India; Prakash V.P., School of Architecture, Christ University, Bengaluru, India Description An account of the resource Migration has played an important role in the transformation of tangible and intangible cultural heritage across regions, including in the Global South, especially among postcolonial nations, due to their longstanding people-to-people contacts leading to socio-economic and historical-cultural transferences over time. In the case of India, global migratory forces have irreversibly transformed its tangible and intangible sociocultural landscape into a form of syncretism reflected in our civilizational ethos of Vasudhaiva Kutumbakam. In this context, this paper explores the role of international in-/outmigration in historical and contemporary times toward the evolution of India's regional cultural identities using a case of Chettiars' migration from the hinterlands of Chettinad in present-day Tamil Nadu, which is in the south of the Indian subcontinent, to the far-flung nation state of Burma/Myanmar in the northeast, including their subsequent return, primarily during the 19th and the 20th centuries. Using the 3i Framework (Interests, Ideas and Institutions), the study explains the role of cross-border migration in shaping the tangible and intangible heritage of Chettinad, as reflected in its architecture, cuisine and social customs. 2026 Association for the Study of Ethnicity and Nationalism and John Wiley &amp; Sons Ltd. Subject The topic of the resource Chettinad; cultural transformation; globalisation; heritage; Indian knowledge systems Publisher An entity responsible for making the resource available Ain Shams University Relation A related resource ISSN: 14738481; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Book Faculty Publications- Book Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Data Science in the Medical Field Description An account of the resource Data science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of data science in healthcare. Every human body produces 2 TB of data each day. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. More sophisticated technology, such as data science, allows clinicians and researchers to handle such a massive volume of data to track the health of patients. The book focuses on the potential and the tools of data science to identify the signs of illness at an extremely early stage. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. Creator An entity primarily responsible for making the resource Kadry S.; Mahajan S. Source A related resource from which the described resource is derived Data Science in the Medical Field, pp. 1-435. Publisher An entity responsible for making the resource available Elsevier Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/C2023-0-01347-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/C2023-0-01347-4</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208856826&amp;doi=10.1016%2FC2023-0-01347-4&amp;partnerID=40&amp;md5=01384df6c5e010bbfdca827b650c1107" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208856826&amp;doi=10.1016%2fC2023-0-01347-4&amp;partnerID=40&amp;md5=01384df6c5e010bbfdca827b650c1107</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-044324028-7; 978-044324029-4 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kadry S., Applied Data Science, Noroff University College, Kristiansand, Norway, Lebanese American University, Beirut, Lebanon; Mahajan S., Postdoc (Norway), Christ (Deemed to be University), Delhi-NCR, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Book Faculty Publications- Book Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource AGRI 4.0 AND THE FUTURE OF CYBER-PHYSICAL AGRICULTURAL SYSTEMS Description An account of the resource Agri 4.0 and the Future of Cyber-Physical Agricultural Systems is the first book to explore the potential use of technology in agriculture with a focus on technologies that enable the reader to better comprehend the full range of CPS opportunities. From planning to distribution, CPS technologies are available to impact agricultural output, delivery, and consumption. Specific sections explore ways to implement CPS effectively and appropriately and cover digitalization of agriculture, digital computers to assist the processes of agriculture with digitized data and allied technologies, including AI, Computer Vision, Big data, Block chain, and IoT. Other sections cover Agri 4.0 and how it can digitalize, estimate, plan, predict, and produce the optimum agricultural inputs and outputs required for commercial purposes. The global team of authors also presents important insights into promising areas of precision agriculture, autonomous systems, smart farming environment, smart production monitoring, pest detection and recovery, sustainable industrial practices, and government policies in Agri 4.0. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. Creator An entity primarily responsible for making the resource Kadry S.; Sharma V.; Dhanaraj R.K.; Jhaveri R.H.; Vendhan G. Source A related resource from which the described resource is derived Agri 4.0 and the Future of Cyber-Physical Agricultural Systems, pp. 1-310. Publisher An entity responsible for making the resource available Elsevier Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/C2022-0-01810-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/C2022-0-01810-9</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198435170&amp;doi=10.1016%2FC2022-0-01810-9&amp;partnerID=40&amp;md5=d4e7e20209980bea3437f8f32617dd3a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198435170&amp;doi=10.1016%2fC2022-0-01810-9&amp;partnerID=40&amp;md5=d4e7e20209980bea3437f8f32617dd3a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-044313185-1; 978-044313186-8 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kadry S., Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon; Sharma V., Department of Computational Sciences, Christ University, Delhi NCR Campus, India; Dhanaraj R.K., Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Maharashtra, Pune, India; Jhaveri R.H., Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, India; Vendhan G., Bharathiar University, Tamil Nadu, Coimbatore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kafila; Ghai, Bhupaesh; Champaneria, Tushar; Sidhu, Kawerinder Singh; Lourens, Melanie; Acharjee, Purnendu Bikash Title A name given to the resource Machine Learning in Investment Analysis-Enhancing or Replacing Human Judgment Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings - 2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies, SISIMPACT 2025;pp.295-300 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/SISIMPACT67725.2025.11439080" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/SISIMPACT67725.2025.11439080</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037469838?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037469838?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kafila, School of Business, SR University, Telangana, Warangal, India; Ghai B., University Institute of Computing, Chandigarh University, India; Champaneria T., L. D. College of Engineering, Computer Engineering, Ahmedabad, India; Sidhu K.S., Uttaranchal Institute of Management, Uttaranchal University, Uttarakhand, Dehradun, India; Lourens M., Durban University of Technology, Faculty of Management Sciences, South Africa; Acharjee P.B., CHRIST University, Computer Science, Bengaluru, India Description An account of the resource Machine Learning (ML) involvement in investment analysis is quickly revolutionizing the investment-based decisions through becoming highly accurate, quick, and embracing increased data processing capabilities. This paper is to research on whether ML is complementary or a possible replacement to human financial judgment. We run experiments over 1.2 million financial transactions between 150 firms comparing old style analyst recommendations and ML-models, including XGBoost, LSTM and Random Forest. The findings indicate that ML models outperformed prediction capability by 19.6 percent and lowered the volatility of the portfolios by 14.3 percent in 5-year investment. Also, the ML-Aided decision-making was better than human (only) approaches in 78 percent of the cases in markets with high volatility or that involved trading in complicated assets. The qualitative variables like regulatory policy changes and investor sentiment however were too difficult to decipher under the leadership of ML only. Our results indicate that ML supports rather than supers the human judgement and thus demonstrates a hybrid paradigm of decision making that resolves computational exactitude with context sensitive understanding in the modern investment scenarios. 2025 IEEE. Subject The topic of the resource Financial Forecasting; Human Judgment; Hybrid Decision-Making; Investment Analysis; Machine Learning; Portfolio Optimization Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155787-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kafila; Hari Krishna, Somanchi; Kulshrestha, Nitin; Singh Sidhu, Kawerinder; Budiharjo, Roy; Inumula, Krishna Murthy Title A name given to the resource The Impact of AI on High-Frequency Trading: A New Paradigm in Share Market Dynamics Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 International Conference on Pervasive Computational Technologies, ICPCT 2025;pp.452-458 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICPCT64145.2025.10940362" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICPCT64145.2025.10940362</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105002861705?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002861705?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kafila, Sr University, School of Business, Telangana, Warangal, India; Hari Krishna S., Vignana Bharathi Institute of Technology, Department of Business Management, Aushpur Village, Ghatkesar Mandal, Telangana State, 501301, India; Kulshrestha N., Christ Deemed to Be University, Commerce Finance and Accountancy, Uttar Pradesh, Ghaziabad, India; Singh Sidhu K., Uttaranchal University, Uttaranchal Institute of Management, Uttarakhand, Dehradun, India; Budiharjo R., Telkom University, Accounting, School of Economics and Business, West Java, Bandung, 40257, Indonesia; Inumula K.M., Symbiosis International (Deemed University), Symbiosis Institute of International Business (SIIB), Maharashtra, Pune, 411057, India Description An account of the resource A fresh approach in financial trading has emerged as a result of the significant shifts in the dynamics of global share markets brought about by the incorporation of artificial intelligence (AI) into high-frequency trading (HFT). In order to analyse large datasets in real-time, perform trades in microseconds, and AI-driven Deep learning, machine learning, and natural language processing are all examples of things that HFT uses to help people make the best choices they can in markets that have to change all the time. This change will help people make better decisions, and sellers will be able to respond quickly to changes in the market. Individuals can trade faster, better, and for greater amounts of cash with it than ever before. AI does have some problems when used in HFT, though. It can make the market less stable, lead to legal problems, and make it more diligently to be fair and honest. The amount of money, how well markets work, along with the way risks are handled are all changed by AI. It also discusses about how AI changes HFT. This study talks about the pros and cons of HFT powered by AI. Along with the way shares are sold, it also hints at how it might change future rules. 2025 IEEE. Subject The topic of the resource AI; ML; Precision; Share Market; Trading Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833150868-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kafila; Wale, Swati Khanderao; Jain Jacob, M.; Sidhu, Kawerinder Singh; Kulshrestha, Nitin; Dhoke, Satish Manikrao Title A name given to the resource Enhancing Investment Advisory with Machine Learning for a New Era in Financial Services Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings - 2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies, SISIMPACT 2025;pp.217-222 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/SISIMPACT67725.2025.11439233" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/SISIMPACT67725.2025.11439233</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037466894?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037466894?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kafila, School of Business, SR University, Telangana, Warangal, India; Wale S.K., Shree Ramchandra College of Engineering Lonikand, Artificial Intelligence and Data Science, Pune, India; Jain Jacob M., SRM Institute of Science and Technology, Faculty of Management (MBA), Tamilnadu, India; Sidhu K.S., Uttaranchal Institute of Management, Uttaranchal University, Uttarakhand, Dehradun, India; Kulshrestha N., Christ Deemed to Be University, India; Dhoke S.M., Moreshwar Arts Science and Commerce College, Department of Commerce, Maharashtra, India Description An account of the resource The current financial service environment, where the volatility of markets and the need to offer flexible solutions is growing, is starting to challenge the traditional investment advisory models. This paper implements a new framework, which incorporates the most advanced methods of machine learning, to make investment advising a process driven by real data. This is unlike the current models which are overly dependent on historical trends or fixed risk profiles, our system allows us to use real time behavior analytics, sentiment analysis and dynamic portfolio optimization to give hyper personalized investment recommendations. The framework feeds the ensemble learning, attention-based neural networks, explainable AI (XAI) to make sure the transparency, regulatory, and investor trust. The innovation in particular is based on the constant interaction between client and adjustment of the model in terms of a ready and sensitive advisory intervention. The study will not only improve the relevance and precision of financial advice, but will with its informed automation of advisor-client relationship led to a redefinition of the advisor-client relationship. The insights guide to a world of advisory services where ML and machine learning complement strategic decision-making with unheard levels of specificity and individuality. 2025 IEEE. Subject The topic of the resource Explainable AI (XAI); Financial services; Investment advisory; Machine learning; Personalized finance Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155787-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource An IoT-based agriculture maintenance using pervasive computing with machine learning technique Subject The topic of the resource Classification; Crop harvesting; Detection of plant disease; RFO; Threshold segmentation Description An account of the resource Purpose: In cultivation, early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates, ensuring that the economy remains balanced. The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification. In this investigation, the accurate prior phase of crop imagery has been collected from different datasets like cropscience, yesmodes and nelsonwisc. In the current study, the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science, yes_modes, nelson_wisc dataset. Design/methodology/approach: In this research work, random forest machine learning-based persuasive plants healthcare computing is provided. If proper ecological care is not applied to early harvesting, it can cause diseases in plants, decrease the cropping rate and less production. Until now different methods have been developed for crop analysis at an earlier stage, but it is necessary to implement methods to advanced techniques. So, the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation. This implemented design is verified on Python 3.7.8 software for simulation analysis. Findings: In this work, different methods are developed for crops at an earlier stage, but more methods are needed to implement methods with prior stage crop harvesting. Because of this, a disease-finding system has been implemented. The methodologies like Threshold segmentation and RFO classifier lends 97.8% identification precision with 99.3% real optimistic rate, and 59.823 peak signal-to-noise (PSNR), 0.99894 structure similarity index (SSIM), 0.00812 machine squared error (MSE) values are attained. Originality/value: The implemented machine learning design is outperformance methodology, and they are proving good application detection rate. 2021, Emerald Publishing Limited. Creator An entity primarily responsible for making the resource Kailasam S.; Achanta S.D.M.; Rama Koteswara Rao P.; Vatambeti R.; Kayam S. Source A related resource from which the described resource is derived International Journal of Intelligent Computing and Cybernetics, Vol-15, No. 2, pp. 184-197. Publisher An entity responsible for making the resource available Emerald Group Holdings Ltd. Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1108/IJICC-06-2021-0101" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/IJICC-06-2021-0101</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119492141&amp;doi=10.1108%2FIJICC-06-2021-0101&amp;partnerID=40&amp;md5=e3435df6852604760887e596f7867b5e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119492141&amp;doi=10.1108%2fIJICC-06-2021-0101&amp;partnerID=40&amp;md5=e3435df6852604760887e596f7867b5e</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 1756378X Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kailasam S., CSE, Koneru Lakshmaiah Education Foundation, Guntur, India; Achanta S.D.M., ECE, Vignan's Institute of Information Technology, Visakhapatnam, India; Rama Koteswara Rao P., ECE, NRI Institute of Technology, Krishna, India; Vatambeti R., CHRIST (Deemed to be University), Bengaluru, India; Kayam S., ECE, Koneru Lakshmaiah Education Foundation, Guntur, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource COVID-19 outbreak prediction using quantum neural networks Subject The topic of the resource Corona virus; COVID-19; Quantum machine learning; Quantum neural network Description An account of the resource Artificial intelligence has become an important tool in fight against COVID-19. Machine learning models for COVID-19 global pandemic predictions have shown a higher accuracy than the previously used statistical models used by epidemiologists. With the advent of quantum machine learning, we present a comparative analysis of continuous variable quantum neural networks (variational circuits) and quantum backpropagation multilayer perceptron (QBMLP). We analyze the convoluted and sporadic data of two affected countries, and hope that our study will help in effective modeling of outbreak while throwing a light on bright future of quantum machine learning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021. Creator An entity primarily responsible for making the resource Kairon P.; Bhattacharyya S. Source A related resource from which the described resource is derived Advances in Intelligent Systems and Computing, Vol-1279, pp. 113-123. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-15-9290-4_12" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-15-9290-4_12</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098271346&amp;doi=10.1007%2F978-981-15-9290-4_12&amp;partnerID=40&amp;md5=ce0e7de3dad5eb0260c5521637a6f30b" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098271346&amp;doi=10.1007%2f978-981-15-9290-4_12&amp;partnerID=40&amp;md5=ce0e7de3dad5eb0260c5521637a6f30b</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 21945357; ISBN: 978-981159289-8 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kairon P., Delhi Technological University, Bawana, Delhi, India; Bhattacharyya S., CHRIST (Deemed to be University), Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Generative AI and its impact on creative thinking abilities in higher education institutions Description An account of the resource Generative AI technologies such as ChatGPT have started gaining increased popularity among higher education institutions. Students, as well as teaching professionals, can utilize these tools for various academic purposes due to the immense benefits they provide by way of customization of data generated and ease of access to data. However, this chapter seeks to analyze how such tools may impact students' creative thinking ability. It also analyses the drawbacks faced by teachers after implementation of such tools. The methodology adopted for the study was two surveys: one administered to gather students' opinions and the other for understanding teachers' perspectives. The analysis of the data collected shows that the over-reliance of students on such generative AI tools might hinder students' ability to think creatively to some extent. The chapter also suggests some of the strategies that can be adopted by teachers to ensure students' capabilities are assessed accurately. 2024, IGI Global. All rights reserved. Creator An entity primarily responsible for making the resource Kaitharath M.F.; Shifna K.N.; Anuradha P.S. Source A related resource from which the described resource is derived Impacts of Generative AI on Creativity in Higher Education, pp. 141-176. Publisher An entity responsible for making the resource available IGI Global Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3693-2418-9.ch006" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-2418-9.ch006</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207550436&amp;doi=10.4018%2F979-8-3693-2418-9.ch006&amp;partnerID=40&amp;md5=b945a81f796a2fb014a8cecd074ef7d7" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207550436&amp;doi=10.4018%2f979-8-3693-2418-9.ch006&amp;partnerID=40&amp;md5=b945a81f796a2fb014a8cecd074ef7d7</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-836932419-6; 979-836932418-9 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kaitharath M.F., Christ University, Bangalore, India; Shifna K.N., Christ University, Bangalore, India; Anuradha P.S., Christ University, Bangalore, India