Handwritten Telugu Character Recognition Using Machine Learning
character recognition; Convolutional Neural Network (CNN); Handwritten Telugu character recognition
The Telugu language is the most prominent representative within the Dravidian language family, predominantly spoken in the southeastern regions of India. Handwritten character recognition in Telugu has significant applications across diverse fields such as healthcare, administration, education, and paleography. Despite its importance, the Telugu script differs significantly from English, presenting distinct challenges in recognizing characters due to its complexity and diverse character shapes. This study explores the application of machine learning, particularly delving into deep learning techniques, to improve the accuracy of Telugu character recognition. This paper proposes a model to recognize handwritten Telugu characters using Convolutional Neural Network (CNN). The proposed study demonstrates the accuracy in identifying diverse handwritten Telugu characters. We assess the system's performance against conventional and machine learning methodologies and preprocess an extensive dataset to guarantee strong model training. The proposed model excels in accurately predicting visually similar but distinct characters, achieving an impressive accuracy rate of 96.96%. 2024 IEEE.
Karapu B.M.; Anoop G.L.; Elappila M.; Mithun B.N.
International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024
Institute of Electrical and Electronics Engineers Inc.
Karapu B.M., Christ Deemed to Be University, Department of Computer Science & Engineering, Bengaluru, India; Anoop G.L., Christ Deemed to Be University, Department of Computer Science & Engineering, Bengaluru, India; Elappila M., Christ Deemed to Be University, Department of Computer Science & Engineering, Bengaluru, India; Mithun B.N., Christ Deemed to Be University, Department of Computer Science & Engineering, Bengaluru, India
Intelligent Agents System for Vegetable Plant Disease Detection Using MDTW-LSTM Model
Dynamic Time Warping (DTW); Long Short Term Memory (LSTM); Plant Disease Detection
When it comes to agricultural output, nation, India, ranks first in the world, and agriculture is unparalleled. The need to categorize and trade agricultural goods is paramount. Manual organization, which is tedious and laborious, is not a choice. When agricultural products are graded automatically, a lot of time is saved. The application of image processing techniques facilitates the examination and evaluation of the products. A technique for identifying diseased vegetables is the focus of this effort. Feature extraction, preprocessing, segmentation, and training the model are all heavily dependent on sequence. Among the preprocessing technologies at disposal are image segmentation and filtering. Using Kapur's thresholding based segmentation method, the image's sick areas can be located during the segmentation process. Use k-means clustering for feature extraction to identify vegetable plant diseases. The training of an MDTW-LSTM model relies heavily on feature selection. In terms of performance, the proposed method surpasses two cutting-edge algorithms: LSTM and DTW. The results showed an accuracy of 97.35 percent, indicating a remarkable improvement. 2024 IEEE.
Chaturvedi A.; Nadgaundi S.K.; Raja M.K.; Dubey V.R.; Singhal A.; Gupta V.
2024 3rd International Conference for Innovation in Technology, INOCON 2024
Chaturvedi A., Gla University, Department of Electronics and Communication Engineering, Uttar Pradesh, Mathura, India; Nadgaundi S.K., Bharati Vidyapeeth College of Engineering, Instrumentation Engineering Department, Navi Mumbai, India; Raja M.K., Sri Eshwar College of Engineering, Department of Computer Science and Engineering, Coimbatore, India; Dubey V.R., Jai Narain College of Technology, Department of Computer Science and Engineering, Lambakheda, Bhopal, India; Singhal A., Christ (Deemed to Be University), School of Sciences, Delhi-NCR, Uttar Pradesh, Ghaziabad, India; Gupta V., Christ (Deemed to Be University), School of Sciences, Delhi-NCR, Uttar Pradesh, Ghaziabad, India
Binary Logistic Regression; Demographic Characteristics; Environmental Education; Higher Education Institutes; Sustainable Society
The need to preserve the environment, lower pollution levels, expand the amount of green space, and encourage environmentally responsible behavior has grown in recent years, all of which will contribute to a more sustainable society. This study seeks to determine the probability that demographic variables of students in higher education in Delhi NCR will influence their desire to participate in environmental education. Binary Logistics Regression has been used on the data gathered from 302 respondents and the model has been found to have been a good one as shown by Omnibus Test. It is found that 'Gender' and 'Field of Study' are the two most significant variables, which have a higher probability impact on students' willingness to join environmental education. Specifically, female students vis-vis male students and students with engineering & and science background vis-vis other students have more chance of joining environmental education courses. 2024 IEEE.
2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2024
https://doi.org/10.1109/ICRITO61523.2024.10522454 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193787815&doi=10.1109%2fICRITO61523.2024.10522454&partnerID=40&md5=8bb16863e4cdda0e4eeb0c906132644e
Ganguly D.G., School of Business & Management, Christ (Deemed to Be University), Delhi NCR, India; Sinha D., School of Business & Management, Christ (Deemed to Be University), Delhi NCR, India; Das T., School of Business & Management, Christ (Deemed to Be University), Delhi NCR, India
A Comprehensive Methodical Strategy for Forecasting Anticipated Time of Delivery in OnlineFood Delivery Organizations
Ability to monitor; Food surveillance; Monitoring system; Monitoring system; Security
Determining the cost of shipping has long been a cornerstone of urban logistics, but today's effective outcomes need acceptable precision. Around the globe, internet-based meal ordering and distribution services have surpassed public expectations; for example, in India, platform-to-consumer distributions and delivery of food and drinks reached an astounding amount of more than 290 million transactions in 2023. Businesses are required to provide customers with precise details on the time it will take for their food to be delivered, starting from the moment the purchase is placed until it reaches the customer's door. Customers won't place orders if the result measure is greater than the actual delivery date, but a greater number of consumers are going to contact the customer service line if the period of waiting falls shorter than their actual shipment period. This study's primary goals are to identify critical variables that affect the availability of nutritious food inspiring leaders as well as to provide an approach for making accurate predictions. Analyzing and contrasting the primary effects and challenges of distribution and shipping in the nation's many different sectors. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-958 LNNS, pp. 555-563.
Springer Science and Business Media Deutschland GmbH
Kumar S., Department of LPM, Bihar Animal Sciences University, Patna, India; Kumari S., Department of LPM, Bihar Animal Sciences University, Patna, India; Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Ghaziabad, India; Vats P., Department of CSE, Manipal University Jaipur, Rajasthan, Jaipur, India
Cultivating Digital Fields: A Cloud-Centric Blueprint for Stakeholder Engagement in the Indian Agriculture
Cloud computing; E-agriculture; E-governance; ICT in agriculture; Stakeholder engagement
This paper examines the potential of cloud computing to revolutionize the Indian agricultural sector, government operations, and rural connectivity. It highlights the benefits and challenges associated with cloud computing in agriculture and proposes a structured model to implement it effectively. Cloud computing allows farmers to access real-time information, make informed decisions, and improve access to markets. The paper examines the difficulties and advantages of cloud computing for the government in transitioning to a cloud-based version of itself for its operations. Additionally, it draws attention to specific areas related to the agricultural sector in India and certain applications offered by the government to enhance the consumer experience for stakeholders. The Government of India has demonstrated its commitment to developing technology-driven agriculture through e-NAM, Kisan Suvidha, and Agri-market initiatives. However, some challenges must be addressed to ensure the successful adoption of cloud computing in the agricultural sector. The proposed implementation model outlines the essential stages of the process, including the needs assessment, the selection of cloud providers, the automation of workflow, the modernization of applications, the implementation of security measures, and the implementation of continuous improvement. The model emphasizes the importance of training, feedback mechanisms, and collaboration. Furthermore, the paper underscores the need for a specific feedback system and grievance redress for agricultural cloud applications to enhance user experiences. To reap the full benefits of cloud computing in the Indian agricultural sector, a comprehensive strategy is necessary. This strategy necessitates technology adoption, awareness-raising, education, and stakeholder engagement. Utilizing cloud technologies, the Indian agricultural sector can realize sustainable growth, increased efficiency, and equitable development. This paper emphasizes the importance of cloud computing in transforming the Indian agrarian landscape. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-916 LNNS, pp. 243-252.
Mitra S., School of Commerce, Finance and Accountancy, CHRIST (Deemed to Be University), Bangalore, India; Agrawal A., School of Commerce, Finance and Accountancy, CHRIST (Deemed to Be University), Bangalore, India; Joseph J.J., School of Commerce, Finance and Accountancy, CHRIST (Deemed to Be University), Bangalore, India
AI Driven Finite Element Analysis on Spur Gear Assembly to Enhance the Fatigue Life and Minimized the Contact Pressure*
AI-based; Contact pressure; Fatigue life; FEM analysis; Hysteresis stressstrain; Spur gear; Structural analysis
The major goal of the current research is to carry out mathematical and finite element analysis on spur gear assemblage to improve fatigue life as well as minimize contact pressure among contact teeth by modifying the face width of spur gear. AI automates FEA simulations and analyses, speeding up the design process. The investigation presented above was conducted using three separate 3d models of driving gear. The equivalent stress for the spur gear assembly of design-3 has decreased up to 13.45% in comparison to design-1, and the fatigue life has increased up to 81.59% at 600 N m, according to the results. Further AI models shall predict stress distribution, contact pressure, and other relevant factors in spur gear assemblies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Dwivedi R.; Sharma S.; Patil S.S.; Datere G.; Upreti K.; Haque M.
Lecture Notes in Networks and Systems, Vol-958 LNNS, pp. 535-544.
Dwivedi R., Department of Mechanical Engineering, Sagar Institute of Science and Technology, MP, Taipei City, Bhopal, India; Sharma S., Department Mechanical Department, AEG Consultancy Services Pvt. Ltd, Bhopal, India; Patil S.S., Dr. D.Y. Patil Vidyapeeths Centre for Online Learning, Dr. D.Y. Patil Vidyapeeth, Pune (Deemed to be University), Pune, India; Datere G., Dr. D.Y. Patil Vidyapeeths Centre for Online Learning, Dr. D.Y. Patil Vidyapeeth, Pune (Deemed to be University), Pune, India; Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India; Haque M., Dr. D.Y. Patil Vidyapeeths Centre for Online Learning, Dr. D.Y. Patil Vidyapeeth, Pune (Deemed to be University), Pune, India
Smart Skin Cancer Diagnosis: Integrating SCA-RELM Method for Enhanced Accuracy
Bag of Feature (BoF) and Speeded Up Robust Features (SURFs); Regularized Extreme Learning Machine (RELM); Sine Cosine Algorithm (SCA)
One out of three cancers now is skin cancer, a figure that has exploded in the previous several decades. Melanoma is the worst kind of skin cancer and occurs in 4% of cases. It is also the most aggressive type. The health and economic impact of skin cancer is substantial, especially given its rising incidence and fatality rates. However, with early detection and treatment, the 5-year survival rate for skin cancer patients is much improved. As a result, a lot of money has gone into studying the disease and developing methods for early diagnosis in the hopes of stopping the rising tide of cancer cases and deaths, particularly melanoma. In order to enhance non-invasive skin cancer diagnosis, this research examines a range of optical modalities that have been utilized in recent years. The suggested system uses image processing to identify, remove, and categorize lesions from dermoscopy images; this system will greatly aid in the detection of melanoma, a type of skin cancer. A median filter is employed for preprocessing. Using watershed and clever edge detector, it can segment objects. The BOF plus SURF method is employed for feature extraction. It employs SCA-RELM, which performs better than the other two conventional approaches, to train the model. 2024 IEEE.
Subramanian M.; Taley R.A.; Sampath V.; Kartheeban K.; Palanimuthu K.; Acharjee P.B.
Subramanian M., Sri Sairam Engineering College, Department of Electronics and Instrumentation Engineering, Chennai, India; Taley R.A., College of Engineering and Technology, Maharashtra, Akola, India; Sampath V., Sri Sankara Arts and Science College (Autonomous), Department of Biochemistry, Kanchipuram, India; Kartheeban K., Kalasalingam Academy of Research and Education, Department of CSE, Krishnankoil, Anandnagar, India; Palanimuthu K., Institute of Health Sciences, Pediatric and Child Health Nursing, Dambi Dollo, Ethiopia; Acharjee P.B., Christ University, Pune Lavasa Campus, Karnataka, Bengaluru, India
Catalyzing Security and Efficiency: Blockchains Integration with IoT and Cloud Computing
Blockchain; Cloud computing; Digital forensics; Internet of things
Blockchain technology is a system that combines a number of computer technologies, encryption, shared storage, namely intelligent contracts, consensus processes, and peer-to-peer (P2P) networks. This research project begins with a description of the architecture of blockchains, followed by a comparison of the various consensus techniques used across various blockchain implementations. This studys scope includes a thorough analysis of the entire blockchain ecosystem. Our investigation also explores the complexity of the consensus models built into different blockchain platforms. This research painstakingly dissects these elements to pinpoint crucial elements that are essential for propelling the adoption and development of blockchain technology. In conclusion, our research corrects misconceptions about blockchains expansive potential and helps to direct the development of the technology across a wide range of industries. These results are significant for determining the future direction of blockchains enduring influence. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Kapoor A.; Kapoor S.; Mishra K.; Jain H.; Upreti K.; Verma A.
Lecture Notes in Networks and Systems, Vol-958 LNNS, pp. 457-467.
Kapoor A., Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, Delhi, New Delhi, India; Kapoor S., Department of Computer Science and Engineering, Akhilesh Das Gupta Institute of Technology and Management, Delhi, New Delhi, India; Mishra K., Department of Computer Science and Engineering, Akhilesh Das Gupta Institute of Technology and Management, Delhi, New Delhi, India; Jain H., Department of Computer Science and Engineering, Akhilesh Das Gupta Institute of Technology and Management, Delhi, New Delhi, India; Upreti K., Department of Computer Science, CHRIST (Deemed to be University), Ghaziabad, India; Verma A., Department of Computer Science and Engineering, Akhilesh Das Gupta Institute of Technology and Management, Delhi, New Delhi, India
Exploring the Balance Between Automated Decision-Making and Human Judgment in Managerial Contexts
Artificial intelligence; Machine learning; Managerial decision-making
The study delves into the dynamic and evolving discussion surrounding the balance between automated and human judgment within the realm of managerial decision-making. The primary objective of this research is to gain insight into how AI is evolving to mitigate ethical biases that are inherent in managerial decision-making. To accomplish this goal, the study adopts a theoretical approach, supported by qualitative analysis through an extensive review of existing literature. By systematically investigating AI techniques for managerial decision-making, the research contributes to a broader understanding of how AI is progressing to promote ethically sound managerial decisions in future. The findings from this study are pertinent to business leaders, policymakers, and researchers, offering guidance as they navigate the intricate relationship between automation and human judgment in todays managerial landscape. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-946, pp. 159-167.
Alex V.P., CHRIST (Deemed to be University), Bengaluru, India; Anirudh H.V., CHRIST (Deemed to be University), Bengaluru, India; Siby A., CHRIST (Deemed to be University), Bengaluru, India
Experimental Design of Interoperable Smart Lighting for Elderly Care
Circadian Rhythm; Elderly Care; Smart Home; Smart Lighting
Smart Home attains an active role in elderly care. Vision impairments caused by aging makes elders more dependent and affects the circadian rhythm or body clock. Some vision impairments can be improved by providing additional lighting. Smart lighting is the leading solution in providing adequate quality of lighting which helps elders to perform their daily activities independently. Various smart lighting solutions for elderly care are proposed in past and failed to consider about the energy loss due to over lighting. Additionally, the solutions are more independent in nature and not integrable to existing smart home solutions. To provide a solution to these ongoing challenges, an experimental design has been proposed to manage the adequate quality lighting for elderly people by controlling the illuminance and color temperature of the light with a feedback mechanism. Also, this experiment has integrated into a popular smart home platform. The proposed design keeps monitoring the ambient lighting and maintains the room's illumination as required for elderly individuals. The functional behaviors of the experimental design are evaluated using a testbed. The result shows that the proposed design reduces the energy usage more than 50% along with providing adequate lighting for elderly individuals. In addition, this experimental design promises that the proposed method can be easily integrated into any existing smart home solutions with its native scripting framework. 2024 IEEE.
Proceedings of the 2nd IEEE International Conference on Networking and Communications 2024, ICNWC 2024
Sadasivam B., CHRIST (Deemed to be University), Department of Computer Science, Bangalore, India; Rajamohan K., CHRIST (Deemed to be University), Department of Computer Science, Bangalore, India
5G-UFMC System For PAPR Reduction Using SRC-Precoding With Different Numerologies
5G; and Beyond 5G (B5G); Peak-to-Average Power Ratio (PAPR); SRC-Precoding; Universal-Filter-Multicarrier (UFMC)
Universal Filtered Multicarrier (UFMC) has been incorporated in 5G and is likely to be considered in future generations (B5G). The prominent limitation of UFMC manifests as a high Peak-to-Average Power Ratio (PAPR). Our suggested approach to address the Peak-to-Average Power Ratio (PAPR) issue in UFMC signals involves the application of diverse precoding matrices, including Square Root Raised Cosine Function (SRC), Discrete Cosine Transform (DCT), and Discrete Hartley Transform (DHT).This technique reduces the PAPR performance of UFMC signals over current state of the art methods. In square root raised cosine (SRC) precoding techniques, a novel precoding matrix is adapted for minimizing PAPR and improvement of BER respectively. Results show that the different subcarrier was applied and surpasses all existing techniques in reduction of PAPR and BER improvement. A novel SRC-Precoding technique reduces PAPR by 5dB for considering 512 sample points with QAM modulation as compared to 10dB for the conventional technique. Additionally, the Bit Error Rate Performance is maintaining 14dB when compared to conventional technique. Furthermore, the evaluation of Bit Error Rate (BER) performance and Peak-to-Average Power Ratio (PAPR) in the UFMC system reveals superior results compared to conventional technique. 2024 IEEE.
Proceedings of the 2024 10th International Conference on Communication and Signal Processing, ICCSP 2024, pp. 559-564.
Ponmani Raja M., Christ (Deemed To Be University), Bengaluru, India; Sujatha S., Christ (Deemed To Be University), Department of ECE, Bengaluru, India; Prajoon P., Jyothi Engineering College, Department of ECE, Thrissur, India
Single-Stage Bidirectional Three-Level AC/DC LLC Resonant Converter with High Power Factor
AC-DC converters; LLC resonant converters; PFC; soft switching
The increasing demand for efficient and high-performance power converters in electric vehicle technology and renewable energy integration has brought attention to LLC resonant converters due to their advantages in soft switching, inherent short circuit and open circuit protection, and high efficiency. These converters are particularly well-suited for high-frequency operation, making them ideal for electric vehicle battery charging and other power conversion tasks. However, when integrated with a front-end boost power factor correction (PFC) stage in AC-DC applications, challenges arise in maintaining power balance during transients, leading to voltage fluctuations and potential operational instability. Moreover, light load conditions can result in excessive switching frequencies, causing elevated switching losses and control difficulties. Additionally, traditional LLC resonant converters face limitations related to high voltage stress on switches, which affects device reliability and overall converter performance. To address these issues, researchers have explored the use of multilevel inverters, but they introduce complexity and cost. In this context, this paper proposes a novel single-stage, three-level bidirectional AC-DC LLC-based resonant converter with features like zero voltage switching and duty ratio control for output voltage regulation. The converter achieves a unity displacement power factor naturally through discontinuous conduction mode. Simulation results demonstrate the converter's effectiveness of the proposed topology. The proposed converter offers a promising solution for Electric vehicle chargers, combining unity power factor operation and efficient bidirectional power flow control in a single topology. 2024 IEEE.
Proceedings - 2024 2nd International Conference on Smart Technologies for Power and Renewable Energy, SPECon 2024
https://doi.org/10.1109/SPECon61254.2024.10537247 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195536270&doi=10.1109%2fSPECon61254.2024.10537247&partnerID=40&md5=e0a896c23d2159de4c64e4a0de6c513e
Hrudhya Kurian C., Christ University, School of Engineering & Technology, EEE Department, Bengaluru, India; Peter A.K., Institute for Power Electronics, Hochschule Kempten University of Applied Sciences, Germany
Synergy Unleashed: Smart Governance, Sustainable Tourism, and the Bioeconomy
Bioeconomy; Environmental sustainability; Smart Governance; Sustainable tourism
This study investigates the transformational potential of smart Governance in the tourism sector to enhance the operational effectiveness, transparency, and efficacy of governmental actions. This research synthesises the body of knowledge regarding the use of technology and data-driven methods in Governance using a literature review methodology. A conceptual framework is suggested to highlight the complex effects of smart Governance on many stakeholders in the travel industry. The study uses a multidimensional paradigm that includes agile leadership, stakeholder alliances, network management, and adaptive Governance. It explains how these complementary components construct a revolutionary ecology that encourages creativity, adaptability, and inclusive growth. Organisations can acquire insights into visitor behaviours, preferences, and traffic patterns by utilising data analytics and digital platforms, which can improve resource allocation, infrastructure construction, and policy formation. Applications that use real-time data enable dynamic crowd control, traffic optimisation, and safety improvements. The report also highlights how local communities may be involved in smart Governance to promote inclusive decision-making. This framework helps promote deeper study into the actual application and outcomes of smart Governance, which has the potential to change the travel sector. This multidisciplinary approach fosters resilience, innovation, and responsible, inclusive development. This study promotes real-world applications that fully utilise this synergy to further the interconnected objectives of sustainable tourism, bioeconomic growth, and efficient Governance. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-941 LNNS, pp. 411-419.
George G., Academics, Westford University College, Sharjah, United Arab Emirates; Varghese B., Tourism Management, School of Business and Management, Christ University, Bangalore, India; Kulkarni M., Symbiosis Centre for Information Technology (SCIT), Symbiosis International (Deemed) University, Pune, India
Abusive Words Detection on Reddit Comments Using Machine Learning Algorithms
Abuse words; CNN; Gradient Boosting Machine and random forest; SVM; XGB classifier
Utilization of artificial intelligence contributes to the efficient examination of emotions, resulting in valuable insights into the psychological condition of users on a large scale. In this research endeavor, sentiment analysis is conducted on a dataset from Reddit, which was obtained through Kaggle. The feedback in this collection of data was divided into downbeat, neutral, and upbeat sentiments. Various machine learning techniques, like Random Forest, Extreme Gradient Boosting Classifier (XGB), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), were detected and examined to assess their effectiveness in sentiment classification. The review of these techniques comprised performance criteria such as F1 Score, accuracy, precision, and recall. Additionally, confusion matrices were utilized to assess the algorithms' proficiency in identifying abusive language. The investigation's conclusions indicate that, when it comes to sentiment analysis, the random forest method performs better than any other strategy, with a maximum accuracy of 0.99 that is on par with the CNN model's accuracy of 0.98. Moreover, random forest proves to be the most effective algorithm in recognizing negative comments and abusive language. This study underscores the significance of employing machine learning algorithms in sentiment analysis, content moderation, social media monitoring, and customer feedback analysis, emphasizing their role in enhancing automated systems that aim to comprehend user sentiments in online discussions. 2024 IEEE.
Madhurima S.; Abhijith Ajith K.; Vs S.; Prathap B.R.
Proceedings - 2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024, pp. 312-317.
https://doi.org/10.1109/DICCT61038.2024.10532806 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195168310&doi=10.1109%2fDICCT61038.2024.10532806&partnerID=40&md5=4a21e8cff5fccbe31ba79b9e5f98df78
Madhurima S., Computer Science and Engineering Christ (Deemed to Be University), Karnataka, Bangalore, India; Abhijith Ajith K., Computer Science and Engineering Christ (Deemed to Be University), Karnataka, Bangalore, India; Vs S., Computer Science and Engineering Christ (Deemed to Be University), Karnataka, Bangalore, India; Prathap B.R., Computer Science and Engineering Christ (Deemed to Be University), Karnataka, Bangalore, India
A Study on Factors Enhancing Immersive Virtual Reality Experiences
Experience enhancement; Immersion; Technology; Virtual Reality
The objective of this study is to identify the various influential factors of immersive virtual reality (VR) experiences and examine the relationship between the immersion factors (technology, visuals, sound, interaction, and sound) and virtual reality experiential outcomes (satisfaction and loyalty). The survey comprises 412 participants who experienced VR games at the Orion Mall in Bangalore. The study has identified the prominent factors for enhancing the immersive experience. The factors are technology, visuals, sound, interaction, and sound. It also identified that there exists a positive association between VR experiential satisfaction and technology, visuals, sound, interaction, and sound. The results imply that service providers should focus on elevating immersive experience as it is closely associated with VR experiential satisfaction and VR experiential loyalty. This will increase the revisit intention and spread positive word of mouth about the virtual experiences. This paper provided valuable insights that pay way to analyze the association between immersion factors and VR experiential outcomes. 2024 IEEE.
2nd International Conference on Artificial Intelligence and Machine Learning Applications: Healthcare and Internet of Things, AIMLA 2024
Rina Daline T., CHRIST (Deemed to Be University), Bangalore, India; Thomas J., CHRIST (Deemed to Be University), Bangalore, India
A Cognitive Architecture Based Conversation Agent Technology for Secure Communication
Agent Technology; Case Base Reasoning Systems; Decision Support System; JADE; Multi-Agent
This paper outlines a multi-agent system-based approach to provider selection. Suppliers in the supply chain are different and the demand and supply levels are high. Buy agents will find the right supply agent in our approach. First, the multi-layer classification system is used to rationally arrange and overall selection on suppliers and buyers. Secondly, the purchase information is organized by the supplier agent to improve device performance. The assessment process is then used to select the suppliers initially. In addition to selecting the correct provider and maximizing the value of the purchaser, the time negotiating mechanism is implemented. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Communications in Computer and Information Science, Vol-2122 CCIS, pp. 1-12.
Preety, Christ University Delhi NCR, Ghaziabad, India; Singh J., Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation, Andhra Pradesh, Vaddeswaram, India; Yadav M., Galgotias College of Engineering and Technology, Uttar Pradesh, Greater Noida, India
AR and Online Purchase Intention Towards Eye Glasses
Augmented reality; e-commerce; eyeglasses; online purchase intention
Augmented reality (AR) can be a potent tool for Indian online eyewear marketers by bridging the gap between online and offline purchasing experiences and meeting the needs of social validation and sensory engagement, which are preferences of Indian consumers. The present research explores how augmented reality (AR) technology affects Indian consumers' intentions to buy glasses online. A combination of descriptive and exploratory research design was used on the sample size of 236 consumers. Data was analyzed using frequency table and Structured Equation modelling (SEM) to identify the relationship amongst the variables. The findings indicate that accessibility to product information, telepresence, and perceived ease of use are important variables impacting purchase intention. AR can bridge the gap between online and offline experiences, meet consumer preferences, and create trust and confidence. Future research should explore AR's effectiveness and personalization possibilities for Indian online eyewear retailers. Future research should explore AR's effectiveness and personalization possibilities for Indian online eyewear retailers. 2024 IEEE.
Prasad A.S.; Sinha D.; Goyal S.; Mathur S.
Prasad A.S., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Sinha D., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Goyal S., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Mathur S., School of Business and Management, Christ (Deemed to Be University), Bangalore, India
A Hybrid Grayscale Image Scrambling Framework Using Block Minimization and Arnold Transform
Decoding; Encoding; Entropy; Image Confusion; Image processing
Image disarranging is the process of randomly rearranging picture elements to make the visibility unreadable and break the link among neighboring elements. Pixel values often don't change while they are being scrambled. There has been a slew of proposed image encryption techniques recently. The two steps that most image encryption algorithms go through are confusion and diffusion. Using a scrambling technique, the pixel positions are permuted during the confusion phase, and an inverse-able function is used to modify the pixel values during the diffusion phase. A good scrambling method practically eliminates the high relationships between adjacent pixels in a picture. In the proposed scheme, XOR based minimization operator is applied on blocks of images followed by Arnold Transform. The suggested design is assessed using a matrix comprising the Structured Similarity Index and the Peak Signal to Noise Ratio. The computed PSNR value less than 10 indicates the input image and scrambled image has high variation. The SSIM value nearer to 0 indicates no similarity in the structure of the input image and scrambled image. 2024 IEEE.
Shiju Thomas M.Y., Rajagiri College of Social, Sciences(Autonomous), Department of Computer Science, Kerala, India; Krishna A.V.N., School of Engineering and Technology, Christ (Deemed to Be University), Department of Computer Science and Engineering, Karnataka, Bangalore, India
Interpreting the Evidence on Life Cycle to Improve Educational Outcomes of Students Based on Generalized ARC-GRU Approach
Academic performance; Educational Outcomes; Generalized Autoregressive Conditional Heteroskedasticity (GARCH)
Research on the effects of teachers' fatigue on students' learning has been significantly less common than research on the effects of teachers' fatigue on teachers' own performance. Therefore, the purpose of this research is to see if teachers' emotional weariness has any bearing on their students' performance in the classroom. Consideration is given to a student's grades and their impressions of whether or not the system receive assistance from teachers, as well as to the student's general outlook on school, confidence in their own abilities, and faith in the availability of faculty support. Data preparation, feature extraction, and model training are the first steps in the proposed approach. Indicators of the quality of the education being provided are eliminated (by outlier removal and feature scaling). k-mean clustering approach is a technique of clustering which is commonly used in feature extraction. Following feature extraction, GARCH-GRU models are trained. The proposed approach is superior to two popular alternatives, ARCH and GRU. Using the provided method, the system were able to achieve a maximum accuracy of 97.07%. 2024 IEEE.
Madumidha S.; Tripathi A.; Srinivasan P.; Wategaonkar S.R.; Chauhan A.; Suganthi D.
Madumidha S., Sri Krishna College of Technology, Department of Information Technology, Coimbatore, India; Tripathi A., Mangalmay Institute of Management and Technology, Dehi NCR, Gr.Noida, India; Srinivasan P., Sona College of Technology, Department of Ece, Tamilnadu, Salem, India; Wategaonkar S.R., Bharati Vidyapeeth College of Engineering, Department of Electronics and Telecommunication, Navi Mumbai, India; Chauhan A., Christ (Deemed to Be University), School of Sciences, Department of Life Sciences, Karnataka, Bengaluru, India; Suganthi D., Saveetha College of Liberal Arts and Sciences, Simats, Department of Computer Science, Chennai, India
Strengthening the Security of IoT Devices Through Federated Learning: A Comprehensive Study
Adversarial Intrusions; Device Tampering; Federated AI Technology Enabler (FATE); Federated Learning; Inclusion/Exclusion Criteria; Internet of Things (IoT); Sensor-Data Manipulation
There is a strong need for having an operative security framework which can help in making IoT (Internet of Things) devices more secure and reliable which can further protect from adversarial intrusions. Federated Learning, due to its decentralized architecture, has emerged as one of the ideal choices by the research practitioners in order to protect sensitive data from wide IoT-based attacks like DoS (Denial of Service) attack, Device Tampering, Sensor-Data manipulation etc. This paper discusses the significance of federated learning in addressing security concerns with IoT (Internet of Things) devices and how those issues can be minimized with the use of Federated Learning has been deliberated with the help of comparative analysis. In order to perform this comparative analysis, we investigated the published work in FL based IoT application for the last five years i.e., 2018-2022. We have defined a few inclusion/exclusion criteria and based on that we selected the desired paper and provided a comprehensive solution to IoT based applications using FL approach. Federated learning offers an optimistic approach to intensify security in IoT environments by enabling collaborative model training while preserving information privacy. In this paper a framework named Federated AI Technology Enabler (FATE) has been envisaged which is one of the recommended frameworks in safeguarding security and privacy measures of IoT devices. 2024 IEEE.
Raj A.; Sharma V.; Rani S.; Shanu A.K.; Kumar N.
Raj A., Scse Galgotias University, Greater Noida, India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi-NCR, India; Rani S., Amity Institute of Information Technology, Amity University, Auup, Noida, India; Shanu A.K., Scse Galgotias University, Greater Noida, India; Kumar N., School of Computing, Dublin City University, Ireland