9 20 16488 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 Jacob, Ann Mariya; Resmi, K.R.; Vineetha, K.R. Title A name given to the resource Computer Vision for Real-Time Nutrition Scanning in Polycystic Ovary Syndrome Management 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 2026 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2026; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/SCEECS68810.2026.11429906" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/SCEECS68810.2026.11429906</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037934873?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037934873?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 Jacob A.M., Christ University, Department of Computer Science, Karnataka, Bengaluru, India; Resmi K.R., Christ University, Department of Computer Science, Karnataka, Bengaluru, India; Vineetha K.R., Christ University, Department of Computer Science, Karnataka, Bengaluru, India Description An account of the resource PCOS stands for polycystic ovary syndrome and it is widely known to be one of the leading causes of insulin resistance and hormonal imbalance among women within the reproductive age spanning from 8 to 13%. Although integrating a healthy diet is a crucial step while managing PCOS, the outdated and frustrating method of manually food logging slows the process and still lacks accuracy. Improving performance feedback is one of the key areas to ensure adherence to a healthy plan. This is the driving reason behind the development of 'NutriScan-PCOS.' In the PCOS focused Streamlit App, the PCOS diet MobileNet application nutrition model helps to do just that. It is trained on 4000 images of 80 diverse Indian foods for quick identification and portion size estimation with OpenCV. The driving function of the app is to track meals and overall nutrition value, scoring on a dynamic scale on 5 parameters focused on the impact on PCOS. It is designed to be user-centric, with a clear green-yellowred traffic system to ensure guidance is instantaneous and easy. NutriScan-PCOS runs on a Streamlit interface with options like photo upload, live camera, barcode scan, and daily logs-making it easy and practical for everyday use. 2026 IEEE. Subject The topic of the resource Glycemic Index; MobileNetV2; OpenCV; Polycystic Ovary Syndrome (PCOS); Streamlit Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833157116-0; 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 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 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 Karthikeyan, M.P.; Anita, E.A. Mary; Geetha, D. Mohana Title A name given to the resource Automated Detection Model (ADM) for Glaucoma, Exudate and Diabetic Retinopathy (DR) Diagnosis Using Fundus Images 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 2nd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/RMKMATE64874.2025.11042563" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/RMKMATE64874.2025.11042563</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105010450947?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105010450947?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 Karthikeyan M.P., R. M. K. Engineering College, Department of CSE, Chennai, India; Anita E.A.M., CHRIST, Deemed to Be University, Department of CSE, Bangalore, India; Geetha D.M., Sri Krishna College of Engineering and Technology, Department of ECE, Coimbatore, India Description An account of the resource A total of 15 million people in India suffer from blindness yet statistical analysis shows 75% of these cases can be treated. The research shows DR and Glaucoma lead to blindness in India. Long-term diabetes mainly causes diabetic retinopathy which stands as the primary cause of blindness. Glaucoma damages the optic nerve until blindness develops. The digitized format of fundus images provides useful diagnostic information about infected retinas for proper eye disease detection. Eye defect diagnosis at an early stage enables medical care that greatly decreases patient vision loss risk. An ophthalmologist conducted the disease screening process through examination of fundus image abnormalities. Higher rates of DR and glaucoma prevalence do not affect the number of available ophthalmologists for evaluating fundus images so the prevention of diseases has been delayed. An automated analytical system should be developed presently to help ophthalmologists enhance their diagnostic process efficiency. The paper introduces an artificial learning methodology that utilizes concatenate systems to detect input fundus images in three categories namely ND and GI and EI and DRI. No Diseases (ND), ii. Glaucoma (GI) iii. The classification groups include Exudate infected Images (EI) along with two other categories namely Glaucoma (GI) and DR Images (DRI). The proposed model Automated Detection Model (ADM) starts by analyzing input samples with histogram-based model and employs DenseNet121 and Inception-ResNetV2to facilitate further processing. The Convolution Neural Networks (CNN) function gathers and sorts the feature extraction data obtained from both models. The proposed approach demonstrates improved accuracy and recall plus average precision when used instead of a solitary model. The proposed machine-learning approach using fundus images proves successful for Glaucoma, Exudate and DR diagnosis according to this experiment. 2025 IEEE. Subject The topic of the resource accuracy; Convolution Neural Networks; Diabetic Retinopathy (DR); Fundus image; Glaucoma Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159848-8; 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 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 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 Gaikwad, Nilima; Vijayalakshmi, S. Title A name given to the resource Etiology and Advanced Detection Techniques for Fetal Brain Abnormalities: A Comprehensive Study 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 10th International Conference on Research in Intelligent Computing in Engineering, RICE 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/RICE67503.2025.11439996" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/RICE67503.2025.11439996</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037436581?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037436581?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 Gaikwad N., School of Sciences, CHRIST UNIVERSITY, Department of Computer Science, India; Vijayalakshmi S., School of Sciences, CHRIST UNIVERSITY, Department of Computer Science, India Description An account of the resource In the modern world, women's health deserves greater focus, particularly in a country like India and particularly during pregnancy. The health of mother's as well as baby's is important throw-out this process. According to a survey, three cases out of a thousand have abnormalities in fetal brain. The likelihood of survival of mother and child significantly increased by early identification of these diseases. Several procedures, including fetal ultrasound, MRI, fetal echocardiography etc. must be carried out during pregnancy in order to monitor fetal development. Pregnancyrelated MRI-scans always practiced to identify and treat fetal brain disorders in former stages. It is possible to identify, examin fetal brain problems early on by doing a prenatal MRI examination. The diagnosis of issues with fetal brain MRI imaging involves several crucial procedures. Among these are image segmentation; image analysis, which comprises extracting characteristic characteristics, improving image quality, identifying relevant patterns, and categorising images based on predefined standards. Classification determines if an anomaly is present or not. Analysing pictures can be difficult due to the wide range of shapes, spatial arrangements, and intensities that are present. The primary subjects of this work are the review and comparison of various fetal brain malformations, as well as their causes and commonalities. 2025 IEEE. Subject The topic of the resource causes; Fetal brain abnormalities; Fetal MRI; Medical Imaging; Segmentation techniques Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155019-6; 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 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 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 Pattanayak, Soumya Sundar; Kumar, Naveen; Shwetha, M. Title A name given to the resource Design and Realization of a Low-Cost 4 Butler Matrix Antenna Array for C-Band Beamforming 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 Conference Proceedings - 10th IEEE International RF and Microwave Conference, RFM 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/RFM67034.2025.11284436" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/RFM67034.2025.11284436</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105031695935?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105031695935?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 Pattanayak S.S., Department of Electronics and Communication Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India; Kumar N., Department of Electronics and Communication Engineering, Christ University, Bengaluru, India; Shwetha M., Department of Electronics and Communication Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India Description An account of the resource The article addresses the design and modelling of a beam-forming antenna array employing a 4 Butler Matrix Network (BMN) for C-band applications. The proposed low-cost antenna is designed and simulated using CST Microwave Studio Suite (V. 2024), and the prototype is fabricated using an in-house PCB manufacturing process. Simulated results demonstrate that the antenna can form beam patterns in four distinct directions (30, 190). The design achieves a wide -10-db bandwidth of 14.5% at 5.575 GHz, along with a narrow bandwidth of 2.5%, 1.07%, 1%, and 2.72% at 4.327, 4.677, 4.99, and 6.6 GHz, respectively. Also, it exhibits very good gain and considerable radiation efficiency. Furthermore, excellent agreement between simulated and experimental results confirms the validity and effectiveness of the proposed design. 2025 IEEE. Subject The topic of the resource Antenna array; Beamforming; Butler Matrix Network; C-Band; Feed Network Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158521-1; 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 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 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 Mohanty, Aniruddha; Prusty, Alok Ranjan; Porwal, Purva Title A name given to the resource A LLM-Powered Approach for Generating Meeting Minutes from Audio Conversations 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 IEEE International Conference on Recent Advances in Computing and Systems, ReACS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/REACS67479.2025.11413427" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/REACS67479.2025.11413427</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036523927?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036523927?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 Mohanty A., Christ (DEEMED To Be UNIVERSITY), Department of Computer Science and Engineering, Bangalore, 560074, India; Prusty A.R., National Skill Training Institute (NSTI), Bhubaneswar, 751010, India; Porwal P., Mahakal Institute of Technology, Ujjain, 456001, India Description An account of the resource Accurate documentation of meetings is essential for effective communication, decision-making, and accountability across various settings, including corporate environments, academic discussions, research collaborations, project reviews, and multi-stakeholder forums. Manual note-taking is often error-prone and time-consuming, highlighting the need for automated solutions. This paper proposes an LLM-based approach for generating structured Minutes of Meeting (MoM) from audio recordings. The method begins with audio transcription using the Vosk model, followed by grammatical and punctuation recovery to enhance text readability. The preprocessing step cleans and segments the transcribed text. Named Entity Recognition (NER) is applied to identify relevant entities such as names, dates, and organizations. The method then segments the conversation into topical sections aligned with the meeting agenda. A Large Language Model (LLM), named OpenAI, has been used to produce abstractive summaries. An action item extraction module identifies tasks, responsible individuals, and deadlines. A speaker analysis component highlights principal participants in decision-making processes. The system organizes the final output into a structured format, such as JSON or PDF, for easy access and distribution. This end-to-end approach enhances the clarity, accessibility, and utility of meeting documentation, reducing manual effort and improving organizational productivity. 2025 IEEE. Subject The topic of the resource Action items; LLM; MoM; Named Entity Recognition (NER); Summary; Vosk Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833154927-5; 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 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 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 Dsouza, Mithun; Sreenivasa, B.L.; Sathyanarayana, S.; Landage, Rajkumar Prabhakar; Sajiv, G.; Karthik, M. Title A name given to the resource Carbon Footprint Minimization in 5G Networks Using Blockchain Integrated Renewable Energy Management Framework 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 IEEE Pune Section International Conference, PuneCon 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/PuneCon67554.2025.11378716" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/PuneCon67554.2025.11378716</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034698065?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034698065?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 Dsouza M., Christ (Deemed to be University), Department of Computer Science, Karnataka, Bangalore, India; Sreenivasa B.L., School of It, St Aloysius (deemed to be university), Institute of Management and Information Technology, Mangalore, India; Sathyanarayana S., Daksha First Grade College, Department of Computer science, Mysore, India; Landage R.P., Punyashlok Ahilyadevi Holkar Solapur University, N B Navale Sinhgad College of Engineering, Department: Electrical, Maharashtra, Solapur, India; Sajiv G., Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences [SIMATS], Saveetha University, Department of Ece, Tamil Nadu, Chennai, India; Karthik M., K.Ramakrishnan College of Engineering, Department of Ai &amp; Ds, Trichy, India Description An account of the resource The rapid growth of 5G networks has created a huge upsurge in energy consumption, raising severe issues regarding carbon emissions and sustainability. Although current strategies like energy-efficient routing, smart grid optimization, and renewable energy integration have tried to counter these impacts, they suffer from a lack of real-time optimization and decentralized control, leading to inefficient carbon reduction and unacceptable energy cost efficiency. In order to fill these gaps, this study presents a novel framework known as BAREO-LP (Blockchain-Assisted Renewable Energy Optimization using Linear Programming) which incorporates blockchain-based peer-to-peer energy trading, renewable energy (solar and battery) modeling, and an emission-conscious linear programming optimization framework. The developed model is utilized through the Python programming language and evaluated on the publicly available 5G-Energy Consumption Dataset from Kaggle. Experimental outcomes show a 58% decrease in carbon emissions and a 32% cost reduction against conventional grid-based and smart grid scenarios. The method also gains an accuracy gain of 16% in energy demand forecasting with minimized load balancing and resource optimization. The blockchain module provides trust and transparency in peer-to-peer energy trading, making the model scalable and realizable in real-world 5G implementations. This multi-faceted strategy sets a new standard for green communication infrastructures and calls for further investigation in sustainable 5G network planning. 2025 IEEE. Subject The topic of the resource Blockchain; Carbon Emission; Carbon Footprint; Linear Programming; Renewable Energy Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158834-2; 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 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 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 Upadhye, Truptee; Nanjundan, Preethi Title A name given to the resource Federated Multi-Task Learning Using Server-Side Normalized Loss-Based Weighting Method 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 IEEE Pune Section International Conference, PuneCon 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/PuneCon67554.2025.11378701" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/PuneCon67554.2025.11378701</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034681230?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034681230?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 Upadhye T., Christ University, Department of Data Science, Bangalore, India; Nanjundan P., Christ University, Department of Data Science, Bangalore, India Description An account of the resource Federated multi-task learning is an approach where multiple clients collaboratively train related but distinct models on their local data without sharing it, thereby preserving privacy while leveraging collective knowledge. However, participating clients can have very different data distributions, sizes and quality, leading to statistical heterogeneity. This heterogeneity is a major challenge in federated learning, as noisy or inconsistent updates from some clients can slow down convergence or degrade the global model's performance. MOCHA is a seminal federated multi-task learning framework that explicitly models task relationships and optimizes clientspecific models, while addressing system challenges like communication costs, fault tolerance and client dropouts. In this work, we enhance MOCHA with a server-side normalized lossbased weighting technique that focuses on the quality of client updates. Each client in the federated multi-task setup computes its local training loss, which is sent to the server during communication rounds. The server normalizes these losses across clients and assigns adaptive aggregation weights, giving more influence to clients with lower normalized losses and down-weighting noisy or unreliable clients. This design simplifies client-side implementation because all weighting is performed at the server. Experiments on heterogeneous MNIST and CIFAR-10 tasks show that the proposed method achieves a slightly higher final-round average test accuracy (0.5108 vs. 0.5065), reduces average training loss by approximately 2.6% (from 1.1148 to 1.0858), and improves fairness by lowering the standard deviation of client accuracies by about 5% (from 0.3631 to 0.3450) compared to baseline MOCHA. These results indicate that server-side normalized loss-based weighting improves training stability, convergence behavior and crossclient fairness in federated multi-task learning under nonconvex optimization. 2025 IEEE. Subject The topic of the resource convergence speed; data distributions; decentralized learning; Federated multi-task learning; MOCHA algorithm; nonconvex optimization; server-side normalized loss-based weighting Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158834-2; 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 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 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 Anbazhagan, Aravindhan; Aryan; Mittal, Labhanshi Title A name given to the resource Sign Language Diversity and its Impact on Global Communication: Towards a Unified Framework 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 IEEE International Professional Communication Conference ;pp.212-216 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ProComm64814.2025.00048" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ProComm64814.2025.00048</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015808972?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015808972?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 Anbazhagan A., Educerelator (OPC) Pvt Ltd, India; Aryan; Mittal L., CHRIST (Deemed-to-be) University, India Description An account of the resource Sign language is a visually expressive language conveyed through hand gestures and other visual expressions rather than spoken words. Primarily used by individuals with auditory impairments, it is also practiced by many hearing individuals to bridge the gap between the hearing-impaired and hard-of-hearing communities, fostering inclusivity in society. Currently, there are more than 300 different types of sign languages in use, practiced by over 72 million people worldwide. These languages lack a standardized framework, leading to communication, education, and professional integration challenges. This study aims to provide an extensive review of linguistic diversity in sign languages and their impact on communication. It also proposes AI-driven solutions to address these challenges, enabling policymakers, stakeholders, and educators to create an inclusive global community by working toward standardizing sign languages. 2025 IEEE. Subject The topic of the resource Accessibility; AI-driven solutions; communication barriers; inclusion; International Sign Linguistic diversity; sign language; standardization Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISSN: 2158091X; ISBN: 979-833153061-7; CODEN: IPPRF 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 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 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 Vidushi; Tripathi, Joyti K; Gupta, Praveen Kumar; Bhardwaj, Shashank; Seth, Deepti; Tripathi, Nishant Title A name given to the resource Pioneering Security in Healthcare: Reversible Data Hiding for ECG Signals and Patient Information 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 4th IEEE International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2026;pp.45-50 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/PARC68365.2026.11453659" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/PARC68365.2026.11453659</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036861591?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036861591?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 Vidushi, School of Sciences, Christ University, Bengaluru, India; Tripathi J.K., School of Information Technology, IMS Noida, Noida, India; Gupta P.K., School of Computer Science and Engineering, Bennett University, Greater Noida, India; Bhardwaj S., KIET Group of Institutions, Department of Computer Applications, Ghaziabad, India; Seth D., KIET Group of Institutions, Department of Applied Science, Ghaziabad, India; Tripathi N., Alliance University, Department of Computer Science and Engineering, Bengaluru, India Description An account of the resource A population in large across worldwide grieve from cardiac illnesses, leading towards an increase in the popularity of telecardiology. Consequently, a significant quantity of electrocardiogram (ECG) signals and sensitive patient information are communicated over the Internet. Using electrocardiogram (ECG) signals as the host medium, this system safeguards the patient's personal information. Nevertheless, it was not possible to replicate the original electrocardiogram (ECG) signal entirely. Therefore, any alterations that occur on EKG have the potential to lead physicians to make an inaccurate diagnosis, which is something that the patient cannot accept. According to the fundamental viewpoint presented in this study, reversible hidden data must completely hide the patient information and ECG signals when they are present. Discreet patient data leading to their personal attributes should be incorporated into the electrocardiogram which is ECG signal while preserving a high level of visibility. On the other hand, in order to safeguard the confidentiality of the patient and the ECG signal, we employ a single built-in encryption mechanism. The ECG instance which is watermarked is recreated in detail. The findings presented herein provide evidence that the proposed method can be reversed. 2026 IEEE. Subject The topic of the resource ECG signal; High bit rate; Medical data hiding; Prediction error expansion; Reversible data hiding; Water marking Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159183-0; 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 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 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 Addapalli, Krishna Vn; Ancy, P. Title A name given to the resource A Reverse Firewall &amp; Re-Encryption Model Supported Peks Model for Security in Health Based Applications 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 OCIT 2025 - Proceedings 23rd OITS International Conference on Information Technology;pp.286-290 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/OCIT66168.2025.11400215" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/OCIT66168.2025.11400215</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036706359?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036706359?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 Addapalli K.V.N., Cse, Soet, Christ University, Bengaluru, 74, India; Ancy P., Cse, Soet, Christ University, Bengaluru, India Description An account of the resource Combining reverse firewalls and reencryption in a PEKS (Public Key Encryption with Keyword Search) model introduces a powerful way to mitigate malicious client behaviour and enhance privacy, especially in untrusted or semi-trusted environments. A reverse firewall is a client-side monitor or proxy that sits between a cryptographic application and the network. Its goal is to ensure that compromised software does not leak any information, even if the software is compromised. In this context a reverse firewall supported with Proxy re-encryption (PRE) supports a secured scheme for applications which require high level of security in public domain scenarios like Cloud for data sharing forms. For schemes which require good level of Identity applications and also needs the support of data Integrity, reverse firewall associated with Re encryption may be a suitable choice. The model is refined with conditional time stamp in accepting the keys for re encryption process. As this model can use different random numbers at different levels of Reverse firewall and re encryption, makes the work free from Chosen cipher text attacks. Also, the proposed model supports Design, Modelling and security analysis in a real time environment. 2025 IEEE. Subject The topic of the resource Public Key Encryption; Reencryption; Reverse Firewall; search words; Trusted Environments Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833157702-5; 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 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 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 Dabbir, Rupesh; Logeshwaran, J. Title A name given to the resource Integrating Big Data Management with Machine Learning in Cloud Environments 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 Networks and Cryptology, NETCRYPT 2025;pp.443-447 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/NETCRYPT65877.2025.11102667" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/NETCRYPT65877.2025.11102667</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015469770?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015469770?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 Dabbir R., Google LLC, San Jose, CA, United States; Logeshwaran J., Christ University, Department of Computer Science, Karnataka, Bengaluru, 560029, India Description An account of the resource In past years, the consideration of cloud environments for big data management and machine learning techniques has increased exponentially. However, the massive amount of data that is made by companies and Internet of Things (IoT) devices has presented the industry with a challenge in storage. In addition, conventional data management methods are unable to manage the complexity, diversity, and volume of this big data. Consequently, cloud environments integrate big data management and machine learning techniques. Combining big data management through cloud environments equipped with machine learning has multiple benefits. First, it is an efficient way to process and analyze large datasets in a distributed and scalable manner. Second, it helps businesses to provide insights and make time to make datadriven decisions. Thirdly, it enables the system to learn from data on an ongoing basis, which helps improve the quality and accuracy of the data. Now, this integration also enhances the overall performance of cloud systems as data management tasks are automated, minimizing manual efforts. Utilizing Big Data Management and Machine Learning Techniques in Cloud Environment to Address the Drawbacks of Conventional Data Management Approaches and Exploit the Value of Big Data These two technologies, when integrated, will allow the enterprise to manage significant amounts of data and derive important insights to arrive at better decisions. Cloud environments provide the automation and scalability required to push this integration further and change considerable data use in businesses. 2025 IEEE. Subject The topic of the resource Automation; Cloud Systems; Complexity; Conventional Data; Diversity; Internet Of Things Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152605-4; 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 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 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 Sharma, Pradeep; Logeshwaran, J. Title A name given to the resource Real-Time Monitoring and Anomaly Detection in Cloud-Based IoT Networks 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 Networks and Cryptology, NETCRYPT 2025;pp.448-452 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/NETCRYPT65877.2025.11102267" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/NETCRYPT65877.2025.11102267</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015531970?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015531970?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 Sharma P., Service Now, United States; Logeshwaran J., Christ University, Department of Computer Science, Karnataka, Bengaluru, 560029, India Description An account of the resource IoT devices have seen explosive growth, causing a data explosion, which makes it almost impossible to manage and monitor these networks. This has led to a demand for solutions providing real-time monitoring and detection of anomalies in cloud-based Internet of Things (IoT) networks. In cybersecurity, the term real-time monitoring denotes the ongoing analysis of data and network performance to detect potential problems. It allows for the identification of anomalies and possible weaknesses in the system. In contrast, anomaly detection is the process of finding deviations from what is considered normal for a system or data. Within cloud-based IoT networks, this might involve identifying abnormal traffic patterns or unusual activity from devices. To solve these challenges, we propose a cloud computing and machine learning-based solution. IoT devices generate a massive amount of data, which is processed in real-time and stored on the cloudbased infrastructure. Many machine learning algorithms analyze these data algorithms to identify anomalies or threats to security. This solution provides an active early warning detection of security breaches from the network management perspective, along with timely response in the case of abnormal behaviour. It will ultimately result in improved cloud-based networks for IoT devices with regard to reliability, security, and performance. This solution can be a key factor in driving the mass implementation of IoT in various sectors. 2025 IEEE. Subject The topic of the resource considered; cybersecurity; explosive; identification; implementation; impossible; management; monitoring Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152605-4; 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 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 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 Mandal, Ashmit; Hasan, Nafisa; Jain, Arumai; Mohanty, Jnyana Ranjan; Sharma, Vandana Title A name given to the resource Computational Relevance of Model Pruning and Quantization for Low-Powered AI 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 2nd International Conference on Multidisciplinary Research and Innovations in Engineering, MRIE 2025;pp.6-10 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/MRIE66930.2025.11156676" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/MRIE66930.2025.11156676</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018796606?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018796606?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 Mandal A., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneshwar, India; Hasan N., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneshwar, India; Jain A.; Mohanty J.R., School of Computer Application, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Sharma V., Christ University, Computer Science Department, Bengaluru, India Description An account of the resource This study investigates how to make machine learning models more efficient for low-power devices by simplifying their structure and lowering size. Six models were evaluated: feed-forward neural networks (FNN), convolutional neural networks (CNN, notably VGG), decision trees, random forests, support vector machines (SVM), and auto encoders. Each was evaluated in its original form, after pruning and quantization, with a focus on model size, accuracy, and training time (as a measure of energy use). The results indicated that pruning for parameter like model size is greatly minimized with Decision Trees reducing by 95%, while it is observed quantization increases efficiency even more. In case of parameter, accuracy, it declined by about 7%. Results show that VGG model retain more accuracy than others after quantization. Pruning also increased training time, particularly for VGG and SVM models. This research thus provides insights into the trade-offs between model complexity, accuracy, and efficiency, guiding the selection for suitable models in resource-bounded environments. 2025 IEEE. Subject The topic of the resource Edge Computing; Low-Power AI; Model Compression; Pruning; Quantization Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158673-7; 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 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 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 Sundharess, B.; Vikash Krishna, R.; Ss, Gopika; Sinha, Somnath; Donald, Cecil; Paul, Aditi Title A name given to the resource Reinforcement Learning for Quantum Phase Estimation Using Deep Q-Network 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 IEEE Madhya Pradesh Section Conference, MPCON 2025;pp.316-321 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/MPCON66082.2025.11256566" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/MPCON66082.2025.11256566</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105031456433?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105031456433?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 Sundharess B., Christ University, Department of Computer Science, Bangalore, India; Vikash Krishna R., Christ University, Department of Computer Science, Bangalore, India; Ss G., Christ University, Department of Computer Science, Bangalore, India; Sinha S., Christ University, Department of Computer Science, Bangalore, India; Donald C., Christ University, Department of Computer Science, Bangalore, India; Paul A., Banasthali Vidhapith, Department of Computer Science, Jaipur, India Description An account of the resource Quantum Phase Estimation(QPE) is a fundamental quantum algorithm that is used for the estimation of eigenphases of unitary operators. Its main goal is to determine the phase associated with each eigenstate. Usually, it take steps such as prepare quantum states, apply controlled unitaries, inverse quantum fourier transformation, and measurement. This study uses the OpenAI Gym framework to build a customized QPE environment. Here, the phase of a randomly generated target unitary operator is estimated using a quantum circuit. Through interaction with this environment, the DQN agent learns the best course of action to increase phase estimation accuracy. It exhibits more flexibility in noisy environments and reduces estimating mistakes. With its insights and approaches for further study in this area, this effort represents a significant advancement in the use of Deep Reinforcement Learning in quantum computing. A Comparative analysis between IBM Quantum(ibm kyiv) and the Aer Simulator on the OpenAI Gym environment using RL agents has been done. 2025 IEEE. Subject The topic of the resource Deep Q Network(DQN); Eigenphases; IBM Quantum Backend; Noisy Environment; OpenAI Gym; Quantum Circuit; Quantum Optimization; Quantum Phase Estimation(QPE); Reinforcement Learning; Unitary Operator Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151285-9; 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 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 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 Vora, Ansh Jagrit; Jayapriya, J.; Vinay, M. Title A name given to the resource Naksha: A Lightweight Visual Packet Capture Tool Based on Location-Aware Metric 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 2024 4th International Conference on Multimedia Processing, Communication and Information Technology, MPCIT 2024 - Proceedings;pp.53-58 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/MPCIT62449.2024.10892768" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/MPCIT62449.2024.10892768</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105000134683?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105000134683?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 Vora A.J., CHRIST University, Department of Computer Science, Bangalore, India; Jayapriya J., CHRIST University, Department of Computer Science, Bangalore, India; Vinay M., CHRIST University, Department of Computer Science, Bangalore, India Description An account of the resource Naksha is a Visual Packet Analyzer, and Sniffer, which provides multiple visual approaches for real-time packet analysis in a geographical domain which utilizes WebSockets and long-polling based protocols in LeafletJS. It inhibits a global map which is populated by Targets, visited by a home network, these targets are connected by a 'link' whose stroke-width is determined by the packet-length. Links are animated to be approaching at different speeds proportional to their propagation latency and congestion loads. They are also animated to subtly gain translucency as they approach their Time-to-live. These packet links also show the direction of the packet (i.e outgoing, or incoming packets) and they can be classified by colour to display categorical attributes like protocol. The WebSocket based server is loop-backed to the home network to provide for robust and rapid deployment. 2024 IEEE. Subject The topic of the resource Analysis; Communications; Packet Capture; Peering; Routing Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-835037546-6; 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 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 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 Nanjundan, Preethi; Deokar, Ruchira; Ramakrishnan, Jayabrabu Title A name given to the resource Anti-Semitic Content on Social Media Analyses Using a Hybrid Model 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 of the International Conference on Multimedia Information Processing and Retrieval, MIPR;pp.1-6 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/MIPR67560.2025.00008" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/MIPR67560.2025.00008</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105025012240?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105025012240?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 Nanjundan P., Christ University, Department of Data Science, Lavasa, Pune, India; Deokar R., Christ University, Department of Data Science, Lavasa, Pune, India; Ramakrishnan J., College of Engineering and Computer Science, Department of Computer Science, JAZAN University, Saudi Arabia Description An account of the resource In the modern world, people have a variety of channels to freely express their opinions, thoughts, knowledge, and feelings on many topics on social media. However, they abuse this freedom by spewing hate speech based on a person's or group's ethnicity, gender, or religion, caste, sexual orientation, ethnicity, and nationality. Hate speech is the most likely form of expression for hostility and superstition on social media. One of the causes of cyberbullying, which can have an effect on social life on both a national and personal level, is an increase in hate speech. Hateful material has the ability to hurt and an individual and promote social unrest. Social media platforms are unable to monitor every topic that is posted by users, so automated detection of hate speech is a crucial tool. Utilizing machine learning models was first popular. But as the dataset size grows, these models are unable to deliver adequate results. While advanced deep learning models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Encoder Representations from Transformers (BERT), and similar architectures have demonstrated greater reliability and effectiveness. Toxic speech is a typical thing which is floating around on social media in this fast-paced world where social media is a significant part of our lives and influences the thoughts of many people, where people have access to distribute whatever sort of information they want. Fighting this is difficult because hate speech recognition is now essential in modern society. In order to extract the necessary features and determine if a section of news contains hate speech or not, this article uses a hybrid CNN+LSTM architecture. 2025 IEEE. Subject The topic of the resource CNN; Deep Learning; Hate speech; Hybrid model; LSTM; Machine Learning; NLP; RNN; Social networks Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISSN: 27704327; ISBN: 979-833159465-7; 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 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 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 Pavithra, K.; Balakrishnan, R.; Loveline Zeema, J. Title A name given to the resource Next Generation AI-Enhanced Intelligent Imaging for Automated Knee X-ray Interpretation in Osteoarthritis: Front office Integration and Employee Training 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 IEEE International Conference on Modern Electronics Devices and Intelligent Communication Systems, MEDCOM 2025;pp.456-461 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/MEDCOM67532.2025.11404948" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/MEDCOM67532.2025.11404948</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036727841?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036727841?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 Pavithra K., CHRIST (Deemed to Be University), Dept of Computer Science, Bangalore, India; Balakrishnan R., MS Ramaiah University of Applied Sciences, Dept of Hospitality Management, Bangalore, India; Loveline Zeema J., CHRIST (Deemed to Be University), Dept of Computer Science, Bangalore, India Description An account of the resource Osteoarthritis is a long-lasting musculoskeletal ailment in which the degenerative alterations in the cartilage of the articles progressively arise. As the pathological anatomy develops to bone structure degeneration, it results in pain, stiffness and functional impairment. It results in immobility, reduced quality of life, more susceptibility to fractures, hospitalization, and mental health problems in the older adults and postmenopausal women. To address the increasing global prevalence of OA and the necessity to diagnose X-rays of the knee promptly, affordably and accessibly, this paper presents an AI-based imaging system based on the use of Convolutional Neural Networks (CNNs) and compares their results with the existing machine learning models. The framework will be structured to integrate the front-office workflows and streamline the diagnostic process of patient registration to report delivery and establish a well-organized staff training ecosystem aimed at enabling clinical staff to operate the AI-enabled workflow and adapt to it. The study offers a comprehensive, deployment ready, diagnostic platform through advanced automation, user oriented front-office functionality and the consistent upskilling of the workforce, to provide scalable, precise, cost effective OA detection, clinical efficiency, interpretative variability reduction, and workforce transition to scalable intelligent, patient centred care. 2025 IEEE. Subject The topic of the resource Artificial Intelligence (AI) in Healthcare; Clinical Workflow Optimization; Employee Training in AI Systems; Front office Integration; Knee X-ray Interpretation; Osteoarthritis Diagnosis Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833157444-4; 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 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 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 Govind, P Jai; Kumar, Naveen Title A name given to the resource Deep Learning Based Performance Prediction of Sustainable Microwave Absorbers 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 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/MAPCON65020.2025.11426455" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/MAPCON65020.2025.11426455</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036394036?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036394036?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 Govind P.J., CHRIST University, RF and Microwave Research Laboratory, ECE Department, Bangalore, India; Kumar N., CHRIST University, RF and Microwave Research Laboratory, ECE Department, Bangalore, India Description An account of the resource This paper proposed a convolutional neural network (CNN) based deep learning (DL) approach to predict performance of sustainable microwave absorbers. This study explores the transformative potential of DL in predicting and optimizing microwave absorber performance, offering a datadriven alternative to traditional approaches. The absorber is a composite of tea and carbon powder considered as waste mixed in various composition percentages. The measured S21 data is used for training the proposed DL model. The prediction of absorber's S21 performance shows an accuracy of above 98 %. 2025 IEEE. Subject The topic of the resource absorber; CNN; deep learning; microwave; sustainable Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153722-7; 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 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 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 Abhishek, R.; Pillai, Vinay Jha; Thiagaraj, Prabu Title A name given to the resource Inverted LPDA for Broadband Radio Astronomy Observation between 150 and 800 Mhz 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 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/MAPCON65020.2025.11425964" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/MAPCON65020.2025.11425964</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036377877?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036377877?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 Abhishek R., Christ (Deemed to Be University), Dept of Electronics and Communication, Bangalore, India, Raman Research Institute, Bangalore, India; Pillai V.J., Christ (Deemed to Be University), Dept of Electronics and Communication, Bangalore, India; Thiagaraj P., Raman Research Institute, Bangalore, India Description An account of the resource Radio transients are celestial objects that vary their brightness in time. The brightness can vary from a few milliseconds to a few hours and exhibit emissions across Radio waves to X-rays and even in Gamma rays. Sophisticated search techniques such as single pulse search, clustering, advanced AI, and digital signal processing are used to detect the radio signals emitted from these transient sources. A study of the signals from the transient sources helps to understand their origin and nature. This paper describes the details of a new antenna designed to detect radio transients at low frequencies between 150 MHz and 800 MHz at RRI Gauribidanur Observatory. 2025 IEEE. Subject The topic of the resource Beam Pattern; LPDA; Radio Transients; RFI Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153722-7; 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 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 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 Ara, Aftab; Ara, Affreen Title A name given to the resource Smart Synergies: Integrating Technologies for Sustainable Urban Planning and Smart City Education - A Case Study 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 22nd International Learning and Technology Conference: Human-Machine Dynamics Fueling a Sustainable Future, L and T 2025;pp.95-102 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/LT64002.2025.10941090" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/LT64002.2025.10941090</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105002884387?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002884387?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 Ara A., University of Hail, College of Business Administration, Hail, Saudi Arabia; Ara A., Christ University, Computer Science Department, Bangalore, India Description An account of the resource The study focuses on the use of intelligent technologies in sustainable town planning and smart city learning, both of which are needed to solve new urban problems. According to recent studies, there is a knowledge gap about studies related to smart urban planning and smart city studies The purpose of this study is to propose a way of smart city education and sustainable urban planning with necessary modern technology prioritizing citizen engagement and environmental sustainability. The principal objective of the study is to provide research-based recommendations on incorporating field-based activities in the course of smart city education. To accomplish this objective, the study uses five in-depth case studies. Each of the three case studies are real case. The potential implication of the study is to transform urban planning education, thus enhancing the participation of citizens in urban and city development. The findings provide a holistic approach by integrating technology, urban planning, and sustainability to improve urban living for sustainable, future smart city developments. This research hopes to provide insight into how new and existing technologies can create improved and more efficient urban areas across the globe. 2025 IEEE. Subject The topic of the resource augmented reality; citizen engagement; digital twins; IoT; participatory planning; Smart cities; smart technology integration; sustainable urban planning; urban education; urban sustainability assessment Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833153221-5; 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 Format The file format, physical medium, or dimensions of the resource online