Enhancing Human-Computer Interaction with a Low-Cost Air Mouse and Sign Language Recognition System
subject: Air Mouse; CNN; Hand Gesture; Mediapipe; Proton Assistant; Sign Language
description: The purpose of this study is to investigate the development of assistive technologies that are designed to empower people with disabilities by increasing their level of freedom and accessibility. Voice assistants, air mice, and software that recognizes sign language are some of the topics that are specifically covered in this. Those who have impaired fine motor skills can benefit from using air mice since they allow controls to be made by hand gestures. Using machine learning algorithms, sign language recognition software is able to decipher signs with an accuracy rate of over 90 percent, making it easier for people who are deaf or hard of hearing to communicate themselves. By relying solely on vocal instructions, voice assistants like Alexa make it possible to control devices without using your hands. Not only do these technologies have the potential to be revolutionary, but they also confront obstacles in terms of improving identification accuracy and integrating them into common gadgets. In this study, the development and impact of voice assistants, sign language software, and air mice are discussed. More specifically, the paper highlights the potential for these technologies to help millions of people with disabilities all over the world. Additionally, it examines potential enhancements that could be made to these technologies in the future in order to further improve accessibility and inclusivity. This research integrates computer vision and machine learning to create a multimodal system blending air mouse functionality with real-time sign language translation. Achieving 95% accuracy in gesture recognition for air mouse control and 98% accuracy in sign language letter classification using a basic webcam, the system promotes accessible interaction without specialized hardware. Despite limitations in vocabulary and lighting sensitivity, future efforts aim to broaden data training and explore mobile deployment. These advancements hold promise for enhancing natural…
creator: Santhan H.; Sudhakar T.; Joy H.K.
source: Proceedings of International Conference on Circuit Power and Computing Technologies, ICCPCT 2024, pp. 93-98.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835037281-6
format: Online
language: English
type: Conference paper
coverage: Santhan H., CHRIST (Deemed to Be University), Computer Science, Bangalore, India; Sudhakar T., CHRIST (Deemed to Be University), Computer Science, Bangalore, India; Joy H.K., CHRIST (Deemed to Be University), Computer Science, Bangalore, India

Comparative Analysis Study of 43-point and 27-point Buyoff Stations for Stressed Mirror Polishing (SMP) Metrology
subject: Finite Element Analysis; Mirror Stress Relieving Mechanism; Segment Support System; Segmented Telescope; Stressed Mirror Polishing; Thirty Meter Telescope; TMT Hexagonal Segments; Whiffletree Design
description: As a collaborative effort within the Thirty Meter Telescope (TMT) project, India is committed to supplying 84 polished segments for the primary mirror, employing the innovative Stressed Mirror Polishing (SMP) technology obtained from Coherent Inc., USA. SMP allows for the efficient polishing of highly aspheric non-axisymmetrical glass blanks at an accelerated rate. India-TMT (I-TMT) successfully applied SMP to qualify three glass roundels at Coherent's facility in Richmond, CA. The study focuses on a comparative analysis of Buyoff Stations (BOS) used in the SMP process. It contrasts results from the 43-point hydraulic-based BOS at Coherent with simulated outcomes from the 27-point whiffletree-based BOS at I-TMT. This analysis assesses efficacy and performance differences between the two BOS configurations, involving a comprehensive examination of a 1520mm diameter polished glass roundel. The study integrates Finite Element Method (FEM) simulations with experimental data, providing insights into the efficiency of the respective BOS setups. 2024 SPIE.
creator: Basheer A.; Panchal P.; Jha S.K.; Sudharsan K.; Deshmukh P.G.; Valsan V.; Sriram S.
source: Proceedings of SPIE - The International Society for Optical Engineering, Vol-13100
publisher: SPIE
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 0277786X; ISBN: 978-151067523-0; CODEN: PSISD
format: Online
language: English
type: Conference paper
coverage: Basheer A., Indian Institute of Astrophysics, 2nd Block, Koramangala, Bangalore, 560034, India; Panchal P., Indian Institute of Astrophysics, 2nd Block, Koramangala, Bangalore, 560034, India; Jha S.K., Indian Institute of Astrophysics, 2nd Block, Koramangala, Bangalore, 560034, India; Sudharsan K., Indian Institute of Astrophysics, 2nd Block, Koramangala, Bangalore, 560034, India; Deshmukh P.G., Indian Institute of Astrophysics, 2nd Block, Koramangala, Bangalore, 560034, India; Valsan V., CHRIST(Deemed to be University), Hosur Road, Bangalore, 560029, India; Sriram S., Indian Institute of Astrophysics, 2nd Block, Koramangala, Bangalore, 560034, India, CHRIST(Deemed to be University), Hosur Road, Bangalore, 560029, India

Transparency in Translation: A Deep Dive into Explainable AI Techniques for Bias Mitigation
subject: Bias mitigation; Counterfactual explanations; Explainable AI (XAI); Fairness; Layerwise Relevance Propagation; LIME (Local Interpretable Model-agnostic Explanations); Sensitivity Analysis; SHAP (SHapley Additive exPlanations)
description: In an era dominated by artificial intelligence (AI), concerns about bias and discrimination loom large. The quest for fairness and equity in AI-driven decision-making has led to the exploration of Explainable AI (XAI) as a viable solution. This paper undertakes a thorough examination of the bias ingrained within AI systems and posits XAI as a potent antidote. Beginning with an exploration of the origins and aftermath of bias in AI, the analysis traverses the evolution of XAI techniques, including SHAP, LIME, and counterfactual explanations, clearly stating their advantages and drawbacks. With each XAI method thoroughly inspected, the study unravels their applicability across diverse AI models and domains. Furthermore, a compelling case study is presented, showcasing XAI's practical application in a language translation app, where it guarantees transparency and equity in the translation process. This tangible example serves as a testament to XAI's efficacy in mitigating bias within real-world applications. As the analysis concludes, it underscores the pivotal role XAI plays in fostering accountability and trustworthiness in AI systems. By shedding light on how XAI mitigates bias and offering concrete examples of its utility, the paper advocates for its widespread adoption as an imperative step towards the development of ethically robust AI systems. In a landscape filled with concerns about bias, XAI emerges as a beacon of hope, promising a future where AI decisions are transparent, fair, and equitable for all. 2024 IEEE.
creator: Deokar R.; Nanjundan P.; Mohanty S.N.
source: 2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036153-7
format: Online
language: English
type: Conference paper
coverage: Deokar R., Christ University, Department of Data Science, Pune, Lavasa, India; Nanjundan P., Christ University, Department of Data Science, Pune, Lavasa, India; Mohanty S.N., School of Computer Science & Engineering, VIT -AP University, Andhra Pradesh, India

Advancing Predictive Analytics in E-Learning Platform: The Dominance of Blended Models in Enrollment Forecasts
subject: Blend Models; E-Learning Platform; IT Software Courses; Regression Models
description: The rapid expansion of e-learning platforms has revolutionized the landscape of education, particularly highlighting the significance of online courses in contemporary learning environments. This research focuses on Udemy, a prominent online learning platform, and aims to enhance the predictability of course enrollments within its IT & Software category. The study's central purpose is to leverage advanced machine learning techniques to predict course subscriber numbers, a crucial indicator of a course's popularity and success. Employing an extensive dataset from (Kaggle DB)Udemy, encompassing various course attributes such as ratings, reviews, and pricing, the study explores multiple machine learning models. These include Linear Regression, Decision Tree, Random Forest, Gradient Boosting, and K-Nearest Neighbors Regression. A key innovation of this research is the application of ensemble methods, particularly a blended model approach, to integrate predictions from multiple models, thereby enhancing accuracy and reliability. The findings of this study are significant. The ensemble approach, notably the blended model, outperforms individual predictive models in accuracy. Among the single models, Gradient Boosting Regression shows the highest effectiveness in forecasting enrollments. The research highlights the vital role of course characteristics, including ratings and reviews, in determining course popularity. This study contributes to the field of e-learning by introducing a novel, data-driven approach to predict course enrollments. It offers valuable insights for educators, course creators, and platform developers, emphasizing the potential of machine learning in optimizing content strategy and marketing efforts in the digital education domain. The application of ensemble machine learning methods presents a new horizon in educational analytics, paving the way for more nuanced and effective strategies in online education delivery and promotion. 2024 IEEE.
creator: Sumalatha K.N.; Anupama N.; Sasikumar P.; Sucharitha M.M.; Basha M.S.A.
source: 2024 2nd World Conference on Communication and Computing, WCONF 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835039532-7
format: Online
language: English
type: Conference paper
coverage: Sumalatha K.N., PG Department of Business Administration, Maris Stella College (Autonomous), Vijayawada, India; Anupama N., PG Department of Business Administration, Maris Stella College (Autonomous), Vijayawada, India; Sasikumar P., Department of Sciences and Humanities Christ (Deemed to be University), Bengaluru, India; Sucharitha M.M., Department of Professional Studies, Christ (Deemed to be University), Bengaluru, India; Basha M.S.A., GITAM School of Business, Gandhi Institute of Technology and Management (Deemed to be University), Bengaluru, India

Safety of Unmanned Systems
subject: data transmission channels; ground control station; hardware; Information security; software; unmanned aerial vehicles
description: The safety risk management process describes the systematic application of management policies, procedures and practices to the activities of communicating, consulting, establishing the context, and assessing, evaluating, treating, monitoring and reviewing risk. This process is undertaken to provide assurances that the risks associated with the operation of unmanned aircraft systems have been managed to acceptable levels. Active efforts should be made to develop rules to ensure the safe operation of unmanned aerial vehicles. For the safe integration of operations with unmanned aerial vehicles, it is important to take into account the influence of different levels of control and autonomous capabilities, as well as the source of movement monitoring in the system. This article discusses the security issues of unmanned systems, the main directions of ensuring the information security of unmanned systems, software and hardware vulnerabilities have been identified. The methods of information protection are given, the disadvantages are indicated. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
creator: Kononova N.V.; Andrusenko Y.A.; Kononov M.N.; Prakasha G.S.; Tokmakova M.E.
source: Lecture Notes in Networks and Systems, Vol-863 LNNS, pp. 132-140.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-303172170-0
format: Online
language: English
type: Conference paper
coverage: Kononova N.V., North-Caucasus Federal University, Stavropol, Russian Federation; Andrusenko Y.A., North-Caucasus Federal University, Stavropol, Russian Federation; Kononov M.N., North-Caucasus Federal University, Stavropol, Russian Federation; Prakasha G.S., North-Caucasus Federal University, Stavropol, Russian Federation, School of Education, Christ University, Bengaluru, India; Tokmakova M.E., North-Caucasus Federal University, Stavropol, Russian Federation

Enhanced Lumpy Cattle Skin Disease Prognosis via Deep Learning Methods
subject: Deep Learning; ECLAT Algorithm; Lesk Algorithm; Lumpy skin disease; Prediction
description: Animal illness is growing in importance. Identification of the illness is important since various diseases may affect different animals, and immediate guidance will be provided. Cows with lumpy skin issues are caused by the Neethling infection. The affection of these diseases causes lasting injury to the cattle's skin. Reduced Poor growth, reversal, milk production, gravidity, and, in severe cases, mortality are the most common adverse consequences of the illness. We developed a deep learning-based architecture that can predict or recognize disease. A deep literacy system is required to identify the microorganism causing the lumpy skin disease. This system collects diverse cattle electronic medical records and uses data analysis to create an intelligent diagnosis system for cattle diseases. It involves text preprocessing to enhance data quality, and the ECLAT algorithm correlates disease names with probabilities, providing tailored treatment plans. The system ensures timely disease treatment, reducing herders' losses and promoting scientific intelligence in animal husbandry. 2024 IEEE.
creator: Lamani M.R.; Padmaja K.; Shilpa B.L.; Asha V.
source: 2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835037289-2
format: Online
language: English
type: Conference paper
coverage: Lamani M.R., CHRIST(Deemed to be University), Dept of Cse, Bangalore, India; Padmaja K., Gsssietw, Dept of Ise, Mysore, India; Shilpa B.L., Atmece, Dept of Cse, Mysore, India; Asha V., Rns Institute of Technology, Dept of Cse, Bangalore, India

A Secure Deep Q-Reinforcement Learning Framework for Network Intrusion Detection in IoT-Fog Systems
subject: Deep Q-Reinforcement Learning; Fog Computing; Internet of Things; Intrusion detection system; Performance Evaluation
description: IoT-Fog system security depends on intrusion detection system (IDS) since the growing number of Internet-of-Things (IoT) devices has increased the attack surface for cyber threats. The dynamic nature of cyberattacks often makes it difficult for traditional IDS techniques to stay up to date. Because it can adapt to changing threat landscapes, deep Q-reinforcement learning (DQRL) has become a potential technique for ID in IoT-Fog situations. In this paper, an IDS system for IoT-Fog networks based on DQRL is proposed. The suggested solution makes use of fog nodes' distributed computing power to provide real-time IDS with excellent accuracy and minimal latency. With feedback from the network environment, the DQRL agent learns to recognize and categorize network traffic patterns as either normal or intrusive. Adaptive exploration techniques, effective reward functions, and deep neural networks for feature extraction are adopted by the system to improve predictive performance. The evaluation findings show that, in terms of detection accuracy, precision, recall and f-measure, the proposed DQRL provides flexibility to changing threat patterns as compared to conventional IDS techniques. A vast array of cyberattacks, such as malware infections, denial-of-service (DoS) attacks, and command-and-control communications, are successfully recognized and categorized by the system. It is possible that the suggested solution will be crucial in safeguarding IoT-Fog networks and preventing cyberattacks 2024 IEEE.
creator: Bebortta S.; Tripathy S.S.; Sharma V.; Behera J.R.; Nayak A.
source: 2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835037378-3
format: Online
language: English
type: Conference paper
coverage: Bebortta S., Ravenshaw University, Department of Computer Science, Cuttack, 753003, India; Tripathy S.S., Kiit Deemed to Be University, School of Computer Engineering, Bhubaneswar, 751024, India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi NCR Campus, India; Behera J.R., Kiit Deemed to Be University, School of Electronics, Bhubaneswar, 751024, India; Nayak A., Driems University, Department of Computer Sceince & Engineering, Cuttack, 74022, India

Dictionary-Based BPT Compression with Trimodal Encryption for Efficient Fiber-Optic Data Management and Security
subject: Bounded Probability-Based Textual Data Compression; BPC; Compression factor; Compression ratio; End-to-end delay; PDR; PLR; Textual dataset
description: Fiber-optic transmission systems are capable of carrying tens of terabits per second of traffic and thereby form the core infrastructure for all Internet-based services and applications. While fiber-optic communication provides rapid data transfer, it faces the challenge of managing the substantial data volumes generated, stored, or transmitted. In the realm of fiber-optic communication, data interception is straightforward, necessitating robust security measures. One effective solution is compression-based encryption, which combines security with data compression benefits. Encryption safeguards data by transforming it into ciphertext during transmission, rendering it unreadable to attackers without knowledge of the encryption method. Data compression enhances bandwidth efficiency, enabling the efficient transmission of large data volumes using limited bandwidth. In the event of data compromise, attackers must grasp both compression and encryption methods to decipher the information, adding an additional layer of security. In this paper, an encoding technique named the Bounded Probability-Based Textual Data Compression (BPT) algorithm is introduced with trimodal encryption method for securing the short textual data while transferring from source to the destination. The BPT algorithm creates a codeword using a dictionary that assigns binary codes according to character occurrence probabilities in the input data. To decompress, the coding table must be transmitted alongside the compressed data. The trimodal encryption is used as a second tier for securing the data that was compressed using BPT algorithm. The trimodal encryption employs three encryption methods, and data is encrypted using one of these methods during transmission to the destination. The BPT algorithms performance is evaluated using benchmark textual datasets from the Calgary Corpus and the Canterbury Corpus. The experimental results demonstrate the unique characteristics of the BPT algorithm, including…
creator: Alapatt B.P.; Poonia R.C.; Singh C.
source: Smart Innovation, Systems and Technologies, Vol-392 SIST, pp. 641-658.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 21903018; ISBN: 978-981973689-8
format: Online
language: English
type: Conference paper
coverage: Alapatt B.P., CHRIST (Deemed to be University), Delhi NCR, India; Poonia R.C., CHRIST (Deemed to be University), Delhi NCR, India; Singh C., CHRIST (Deemed to be University), Delhi NCR, India

Analysis on thermal sensitivity of 2D Profilometer used for TMT Glass Polishing
subject: 2-Dimensional Profilometer; Glass Roundel; Reference Sphere; Stressed Mirror Polishing
description: TMT adopts Stressed Mirror Polishing (SMP) technology for the polishing of mirror segments. In this process, the meniscus type spherical shape glass blanks are converted in to a desired aspheric shape by spherical grinding and polishing in the stressed condition. After each grinding and polishing activity metrological measurements are done using different metrology tools. The metrology tool named as 2D-Profilometer is used for low frequency error/foam measurements. It consists of 61 high precision length gauges attached to Carbon Fiber Reinforced Polymer (CFRP) sandwiched Aluminum panel of diameter 1.6 meter in spiral direction. The coefficient of thermal of CFRP is very low however, a small delta temperature variation between the top and bottom sheet of CFRP of the panel will lead to panel bowing which will result in increasing power error. Hence, the objective this work is to analyse the thermal sensitivity of the 2D Profilometer. 2024 SPIE.
creator: Panchal P.; Jha S.K.; Basheer A.; Valsan V.; Gajanan Deshmukh P.; Sriram S.
source: Proceedings of SPIE - The International Society for Optical Engineering, Vol-13100
publisher: SPIE
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 0277786X; ISBN: 978-151067523-0; CODEN: PSISD
format: Online
language: English
type: Conference paper
coverage: Panchal P., India TMT Optical Fabrication Facility, Indian Institute of Astrophysics, Karnataka, Bengaluru, 560034, India; Jha S.K., India TMT Optical Fabrication Facility, Indian Institute of Astrophysics, Karnataka, Bengaluru, 560034, India; Basheer A., India TMT Optical Fabrication Facility, Indian Institute of Astrophysics, Karnataka, Bengaluru, 560034, India; Valsan V., Department of Physics, Christ University, Karnataka, Bengaluru, 560034, India; Gajanan Deshmukh P., India TMT Optical Fabrication Facility, Indian Institute of Astrophysics, Karnataka, Bengaluru, 560034, India; Sriram S., India TMT Optical Fabrication Facility, Indian Institute of Astrophysics, Karnataka, Bengaluru, 560034, India, Department of Physics, Christ University, Karnataka, Bengaluru, 560034, India

A Novel Approach to Optimizing Third-Party Logistics Growth through IT, Big Data, and Machine Learning for Superior Supply Chain Management
subject: Big Data Analytics; IT Integration; Logistics Innovation; Logistics Optimization; Predictive Analytics; Real-Time Monitoring; Supply Chain Efficiency
description: The purpose of this research is to explore proper development prospects of 3PL business, particularly focusing on the utilization of Information Technology and big data technologies for improving the solidity of supply chains. The change of the industry that started from the provision of services and then became an integrated solution implies the rising role of IT as one of the means for supply chain improvement. Based on the market study and investigation of customers' behaviors, as well as the contexts of the 3PL industry of the India, this research outlines how the IT and big data analytics can contribute to the operations improvement and innovation of the 3PL. Besides, this paper aims at finding out whether the Big Data analytics can enhance the competitiveness of third party logistics providers in a volatile market. 2024 IEEE.
creator: Shyamsunder C.; Roshani P.; Soni H.; Dankan Gowda V.; Aghav S.; Abdullah I.
source: 2024 2nd World Conference on Communication and Computing, WCONF 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835039532-7
format: Online
language: English
type: Conference paper
coverage: Shyamsunder C., Symbiosis Institute of Business Management, Hyderabad Symbiosis International University, Pune, India; Roshani P., Department of Commerce, Christ University, Karnataka, Bangalore, India; Soni H., Symbiosis Institute of Business Management, Hyderabad Symbiosis International University, Pune, India; Dankan Gowda V., Department of Electronics and Communication Engineering, BMS Institute of Technology and Management, Karnataka, Bangalore, India; Aghav S., Symbiosis Law School, Hyderabad, Symbiosis International (Deemed University), Pune, India; Abdullah I., Department of Computer Science, Al-Turath University College, Baghdad, Iraq

Compact Dual-Band Millimeter Wave MIMO Antenna for Wireless Communication Systems
description: The article presents the compact dual-band MIMO antenna resonating at 27.5 and 32 GHz. The radiating structure is a rose-shape with elliptical slots and a horizontal slit to achieve the above resonances. The MIMO antenna dimension is 6.2 0 mm2, where an edge-to-edge distance of 1.82 mm separates radiating elements. The ground plane has simple slits to suppress the mutual coupling. The simulation results of the MIMO antenna is validated through measured and diversity parameter results. 2024 IEEE.
creator: Singh A.K.; Parveez Shariff B.G.; Jain H.; Ali T.; Pathan S.; Kumar N.; Dongare A.P.
source: IEEE Antennas and Propagation Society, AP-S International Symposium (Digest), pp. 1649-1650.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 15223965; ISBN: 979-835036990-8; CODEN: IAPSB
format: Online
language: English
type: Conference paper
coverage: Singh A.K., Manipal Institute of Technology, Manipal Academy of Higher Education, Dept. of Electronics and Communication Engineering, Manipal, India; Parveez Shariff B.G., Manipal Institute of Technology, Manipal Academy of Higher Education, Dept. of Electronics and Communication Engineering, Manipal, India; Jain H., Manipal Institute of Technology, Manipal Academy of Higher Education, Dept. of Electronics and Communication Engineering, Manipal, India; Ali T., Manipal Institute of Technology, Manipal Academy of Higher Education, Dept. of Electronics and Communication Engineering, Manipal, India; Pathan S., Manipal Institute of Technology, Manipal Academy of Higher Education, Dept. of Information and Communication Technology, Manipal, 576104, India; Kumar N., Christ University, Dept. of Ece, Bengaluru, India; Dongare A.P., Ebs Universitat Wirschaft und Rechts, Strategy and Internation Management, Oestrich-Winkel, Germany

Theoretical Framework for Blockchain Secured Predictive Maintenance Learning Model Using Digital Twin
subject: Blockchain; Digital Twin; Predictive Maintenance
description: The automotive sector benefits from Digital Twins (DTs), software replicas of physical assets or processes. DTs enable engineers and data scientists to obtain deeper insights into the system and solve the most difficult problems faster and more affordably. Blockchain technology is a developing and exciting technology that has the potential to offer DTs monitoring capabilities, strengthening security and enhancing DTs transparency, dependability, and immutability. Intelligent behavior can be integrated into blockchain-based DTs to foresee important maintenance tasks and successfully manage machine functions. Our research involves creating a theoretical framework that leverages emerging technologies such as blockchain, artificial intelligence and DTs to facilitate resolution in the predictive maintenance of industry machines with minimised governing cost. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
creator: Vinodha D.; Jenefa J.; Anita E.A.M.; Babenko M.
source: Lecture Notes in Networks and Systems, Vol-863 LNNS, pp. 55-66.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-303172170-0
format: Online
language: English
type: Conference paper
coverage: Vinodha D., Departmentof Computer Science and Engineering, Christ University, Bangalore, India; Jenefa J., Departmentof Computer Science and Engineering, Christ University, Bangalore, India; Anita E.A.M., Departmentof Computer Science and Engineering, Christ University, Bangalore, India; Babenko M., North-CaucasusCenter for Mathematical Research, North-Caucasus Federal University, Stavropol, Russian Federation

Fire Resistance of Concrete with Partial Replacement of Ceramic Waste and Carbon Fiber as Additives
subject: Carbon fiber; Ceramic waste; Fire resistance; Superplasticizer
description: One of the primary hazards that causes catastrophic damage to properties and peoples lives is fire. Although ceramic garbage is deposited on the land, it is a non-biodegradable waste that pollutes the environment. This study is based on the use of industrial waste products such as ceramic sanitary waste to improve the mechanical qualities of concrete that have been exposed to elevated temperatures. An experimental investigation was carried out on cubes, cylinders, and beams to assess compressive strength, split tensile strength, and flexural strength with fractional replacement of fine aggregates with 10, 20, and 30% of ceramic waste and 0, 1, and 2% of carbon fibers as additives at normal and elevated temperature as per ASTM code recommendations and the results shown as a significant improvement. The strength of M30 grade concrete with partial replacement of fine aggregate with ceramic waste up to 30% and carbon additives up to 2% shows an improvement of compressive strength by 17.56% than conventional concrete. It is also observed that normal M30 grade concrete loses its strength by 49.6% when it is exposed to 600C and with fractional replacement of fine aggregate by ceramic waste by up to 30% and carbon additives by up to 2% shows the loss of strength is decreased up to 22.67%. It shows that it is the probable substitute solution for the secure discarding of Ceramic waste. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
creator: Narasimha Murthy K.N.; Nelda Paul P.; Beevi Mymoon E.M.; Thejas H.K.
source: Lecture Notes in Civil Engineering, Vol-528 LNCE, pp. 621-631.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 23662557; ISBN: 978-981974843-3
format: Online
language: English
type: Conference paper
coverage: Narasimha Murthy K.N., Department of Civil Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Nelda Paul P., Department of Civil Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Beevi Mymoon E.M., Department of Civil Engineering, CHRIST (Deemed to Be University), Bengaluru, India; Thejas H.K., Department of Civil Engineering, CHRIST (Deemed to Be University), Bengaluru, India

Industry Internet of Things (IIoT) Adoption Pressures in SME OEMs
subject: End-user Pull; Environmental Attitude; IIoT adoption; Institutional Pressure; Mediation Analysis; SME OEMs; Structural Equation Modeling
description: Small and Medium Original Equipment Manufacturers (SME OEMs) face challenges in IIoT adoption due to a lack of technical expertise, additional costs, and preferences of the end-users and significant institutional pressures. This research investigates the influence of Environmental Attitude on the Adoption Intention of Industry Internet of Things (IIoT) technologies among Small and Medium Enterprises and Original Equipment Manufacturers (SME-OEMs). This research analyses the effects of End-user Pull and Institutional Pressure in this relationship. A survey of 263 SME OEMs from 11 industrial sectors across 67 cities was conducted using purposive sampling. Structural Equation Modeling (SEM) was used to analyze data, assessing direct and indirect effects. Results show a significant positive relationship between Environmental Attitude and IIoT Adoption Intention. Mediation analysis reveals significant indirect effects through End-user Pull and Institutional Pressure, with complete mediation as the direct effect becomes insignificant. Findings highlight the crucial role of environmental attitude in shaping IIoT adoption intentions among SMEs. A positive environmental attitude drives SMEs to explore IIoT benefits. End-user Pull and Institutional Pressure are key mediators in this process. These insights are valuable for industry stakeholders, policymakers, and SMEs aiming to promote IIoT adoption. Fostering a positive environmental attitude and leveraging End-user Pull and Institutional Pressure can facilitate IIoT adoption. Policymakers can create initiatives to raise environmental awareness and encourage sustainable practices through IIoT. Industry players can form strategic partnerships to support SME OEMs in IIoT adoption. 2024 IEEE.
creator: Despande P.; Kumar Chandar S.; Elangovan N.
source: Proceedings - 2024 8th International Conference on Inventive Systems and Control, ICISC 2024, pp. 650-655.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835038657-8
format: Online
language: English
type: Conference paper
coverage: Despande P., Christ University, School of Business & Management, Karnataka, Bangalore, India; Kumar Chandar S., Christ University, School of Business & Management, Karnataka, Bangalore, India; Elangovan N., Christ University, School of Business & Management, Karnataka, Bangalore, India

Artificial Intelligence for Enhanced Anti-Money Laundering and Asset Recovery: A New Frontier in Financial Crime Prevention
subject: Anti-Money Laundering (AML); Artificial Intelligence (AI); Machine Learning Algorithms; Suspicious Activity Reports (SARs); Transaction Monitoring
description: The incorporation of artificial intelligence (AI) into asset recovery and anti-money laundering (AML) procedures signifies a revolutionary change in the handling of financial crime. This article investigates the use of AI techniques to improve AML compliance, detect suspicious activity, and improve transaction monitoring. Financial institutions can now analyze massive volumes of transaction data in real-time, find anomalies, and lower false positives thanks to AI-based solutions, which include machine learning algorithms and predictive modeling. The research also outlines the difficulties and advantages of implementing AI, such as enhancing the effectiveness and caliber of suspicious activity reports (SARs) while resolving security and privacy issues with data. The study makes the case that AI's capacity to offer collaborative analytics and dynamic risk assessments is essential for the development of AML frameworks and the overall effectiveness of financial crime prevention. 2024 IEEE.
creator: Balaji K.
source: 2nd International Conference on Intelligent Cyber Physical Systems and Internet of Things, ICoICI 2024 - Proceedings, pp. 1010-1016.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-833154066-1
format: Online
language: English
type: Conference paper
coverage: Balaji K., Christ University, Business and Management, Bengaluru, India

Enhanced Multi-Model Approach for Motion and Violence Detection using Deep Learning Methods Using Open World Video Game Dataset
subject: 3DCNN; Attention Mechanism; Deep Learning; IoT; MobilenetSSD; YOLOv8
description: For today's environment, it is extremely important to understand hostility and motion in a variety of contexts, particularly where accidents are concerned. There's also a high safety risk in public places if there is no proper identification of suspicious activities that occur fast and cannot be accurately observed through traditional surveillance systems that rely on constant human monitoring. Although deep learning algorithms have proven useful for detecting anomalies such as fraud recently, there has been little research on real-time crime detection because of issues related privacy when using live data sets. To tackle the key problem of motion and violence detection with current deep learning methods, this work exploits the Open World Game Dataset which provides realistic activities. The reliance on only one technique undermined the previous models' accuracy while this study comes up with various models to raise the detection precision and real-time processing capability. This work applies MobileNet SSD, YOLOv8 (You Only Look Once), and SSD (Single Shot MultiBox Detector) techniques to create a more accurate movement detection system. To identify violent or illegal behavior from videos, 3D convolutional neural networks (3DCNN) will be used alongside attention approaches. A diverse inexpensive training environment that enables simulating. 2024 IEEE.
creator: Agarwal V.; Singh M.; Prathap B.R.
source: 1st International Conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024 - Proceedings, pp. 355-360.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835036501-6
format: Online
language: English
type: Conference paper
coverage: Agarwal V., Coumputer Science and Engineering, Christ(Deemed To Be University), Banglore, India; Singh M., Coumputer Science and Engineering, Christ(Deemed To Be University), Banglore, India; Prathap B.R., Coumputer Science and Engineering, Christ(Deemed To Be University), Banglore, India

Enhancing Small and Medium OEMs' Adoption of IIoT Technologies
subject: Business support; Enhancing IIoT adoption; Industry Internet of Things manufacturer; Operational support; Small and Medium Original Equipment Manufacturers (SME OEMs); Strategic partnerships
description: Small and Medium Original Equipment Manufacturers (SME OEMs) face challenges of high initial costs, lack of skilled workforce, data security concerns, and limited infrastructure for IIoT implementation. This research explores the crucial factors influencing the successful integration of Industry Internet of Things (IIoT) technologies into products and processes of SME OEMs. The study investigates the impact of IIOT Manufacturers' operational and business support, training effectiveness, and awareness of benefits on SME OEMs' adoption intention of IIoT solutions. A survey was conducted among 263 firms operating in 103 different equipment manufacturing operations across 67 cities, representing 11 industry sectors. The participants were SME OEMs, and data were collected to assess the influence of various factors on their willingness to adopt IIoT technologies. The study revealed significant insights into adopting IIoT solutions among SME OEMs. Training provided by IIoT manufacturers was found to have the most substantial impact on the adoption intention. Moreover, awareness of benefits and business and operational support had an equal and notable influence on the adoption intention of SME OEMs. These findings underline the importance of effective training programs and comprehensive support from IIoT manufacturers in facilitating successful IIoT integration. The study's outcomes emphasize the value of fostering strategic partnerships between Small and Medium Original Equipment Manufacturers and Industry IoT Manufacturers. Such collaborations can be pivotal in enhancing IIoT adoption rates among SME OEMs, enabling them to stay competitive in the fast-paced market. 2024 IEEE.
creator: Despande P.; Kumar Chandar S.; Elangovan N.
source: Proceedings - 2024 8th International Conference on Inventive Systems and Control, ICISC 2024, pp. 656-661.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835038657-8
format: Online
language: English
type: Conference paper
coverage: Despande P., Christ University, School of Business & Management, Karnataka, Bangalore, India; Kumar Chandar S., Christ University, School of Business & Management, Karnataka, Bangalore, India; Elangovan N., Christ University, School of Business & Management, Karnataka, Bangalore, India

Analysing the Influence of Activation Functions in CNN models for Effective Malware Classification
subject: activation function; cnn; deep learning; malware classification
description: With the advancement of information technology, malware has become a persistent cyber security concern that targets computer systems, smart devices, and wide networks. Due to flaws in performance accuracy, analysis type, and malware classification methodologies that miss unsuspected malware attacks, malware classification has thus always been a significant concern and a challenging subject. Using the Malimg dataset, which has 9349 samples from 25 different families, this study classifies malware using a deep learning algorithm called a convolution neural network and evaluating the accuracy using a number of activation functions in this study. The proposed CNN model for malware classification achieves a high accuracy rate without the need for complex feature engineering. The model achieved the highest accuracy of 96.93% when using the Rectified Linear Unit (ReLU) activation functions whereas Leaky Relu gives accuracy of 96.76%, Pre relu gives 96.36%, ELU gives 95.72% and tanh gives accuracy of 95.58%. 2024 IEEE.
creator: Kasliwal S.; Vinay M.; Jayapriya J.; Deepa S.; Mahalakshmi J.
source: 2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2024-01-01
rights: Restricted Access
relation: ISBN: 979-835037311-0
format: Online
language: English
type: Conference paper
coverage: Kasliwal S., Christ Univesity, Department of Computer Science, Bangalore, India; Vinay M., Christ Univesity, Department of Computer Science, Bangalore, India; Jayapriya J., Christ Univesity, Department of Computer Science, Bangalore, India; Deepa S., Christ Univesity, Department of Computer Science, Bangalore, India; Mahalakshmi J., Christ Univesity, Department of Computer Science, Bangalore, India

Design and Simulation of 6.2m Wide-Field Telescope for Spectroscopic Survey
subject: Active Optics; Segmented Mirror Telescope; Spectroscopy; Survey; Wide Field
description: The upcoming large astronomical telescopes are trending towards the Segmented Primary Mirror due to technological advancements & manufacturing feasibility. We have designed a wide-field optical IR spectroscopic survey telescope that can deliver spectra of several millions of astronomical sources. The baseline design of this telescope is a 6.2 m segmented primary mirror with hexagonal mirror segments of 1.44m size, intersegment Edge sensors, and soft positioning actuators. The telescope is designed to provide a 2.5deg FOV achieved through a system of wide field corrector lenses with a design residual ~0.2". Also, it delivers an f/3.61 beam suitable for directly feeding optical fibres. A mechanical concept of the telescope is designed with a truss-based mirror cell to support the segmented primary mirror and keep the deformation to a minimum. As the primary mirror is segmented, the deformation due to different disturbances like wind, vibration and thermal effects must be corrected to a nanometer accuracy to make it act like a monolithic primary mirror. This is achieved through an active control system using three actuators and six inter-segment edge sensors. A simulation tool, codeSMT, is built based on the state-space model of a soft actuator with Multiple-Input Multiple-Output (MIMO) capability to incorporate dynamic wind disturbance from the IAO Hanle site and vibration effects. A detailed error multiplier analysis is performed numerically using this tool and is in good agreement with analytical calculations. A parameter sensitivity analysis is performed to fine-tune the primary mirror control system variables. This paper presents the Optical, Mechanical and Active Control system design approach of a 6.2m wide-field telescope currently under conceptual design. 2024 SPIE.
creator: Deshmukh P.; Sriram S.; Chand T.; Kambhala S.; Ramya S.; Joshi R.; Bharat Kumar Y.; Muthhar M.
source: Proceedings of SPIE - The International Society for Optical Engineering, Vol-13094
publisher: SPIE
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 0277786X; ISBN: 978-151067511-7; CODEN: PSISD
format: Online
language: English
type: Conference paper
coverage: Deshmukh P., Indian Institute of Astrophysics, Bengaluru, 560034, India; Sriram S., Indian Institute of Astrophysics, Bengaluru, 560034, India, CHRIST Deemed to be University, Bengaluru, 560029, India; Chand T., Indian Institute of Astrophysics, Bengaluru, 560034, India; Kambhala S., Indian Institute of Astrophysics, Bengaluru, 560034, India; Ramya S., Indian Institute of Astrophysics, Bengaluru, 560034, India; Joshi R., Indian Institute of Astrophysics, Bengaluru, 560034, India; Bharat Kumar Y., Indian Institute of Astrophysics, Bengaluru, 560034, India; Muthhar M., Indian Institute of Astrophysics, Bengaluru, 560034, India

Analysis of Routing Protocols in MANET Networks
subject: Ad-hoc; B.A.T.M.A.N; Babel; BMX7; MANET; OLSRv1
description: The scientific article is a review and comparative analysis of routing protocols for MANETs. The study examines the main protocols connected to mobile ad hoc networks such as B.A.T.M.A.N, BMX7, OLSRv1, Babel and provides a detailed analysis of their characteristics, advantages and disadvantages. To empirically evaluate performance, tests were carried out in a network simulator. The results of the study allow us to draw conclusions about the effectiveness and reliability of each of the monitoring protocols under various operating conditions of MANET. This article is a valuable contribution to the field of MANET research and can be used in the development of new technologies and solutions for mobile wireless networks. The work is relevant and practically significant because it helps researchers and engineers make informed decisions when choosing the optimal routing protocol in MANET networks. The results obtained can be useful in the design of mobile applications, emergency communication systems, transport management and other areas where the efficient operation of wireless networks is important. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
creator: Krivolapova O.; Lapin V.; Shvedunenko D.; Anita E.A.M.
source: Lecture Notes in Networks and Systems, Vol-863 LNNS, pp. 141-151.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 23673370; ISBN: 978-303172170-0
format: Online
language: English
type: Conference paper
coverage: Krivolapova O., North Caucasus Federal University, Stavropol, Russian Federation; Lapin V., North Caucasus Federal University, Stavropol, Russian Federation; Shvedunenko D., North Caucasus Federal University, Stavropol, Russian Federation; Anita E.A.M., CHRIST (Deemed to be University), Bangalore, India