109
20
16481
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Faculty Publications
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Mathur, Smriti; Tewari, Alok; Vishnoi, Sushant; Agarwal, Vaishali
Title
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An integrated model to predict students online learning behavior in emerging economies: a hybrid SEMANN approach
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01-01-2025
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Journal of International Education in Business;Volume;18;Issue;1;pp.102-126
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<a href="https://doi.org/10.1108/JIEB-01-2024-0004" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/JIEB-01-2024-0004</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85211483973?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85211483973?origin=resultslist</a>
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Mathur S., Department of Marketing, Christ University (Deemed to be University), Ghaziabad, India; Tewari A., School of Management, Babu Banarasi Das University, Lucknow, India; Vishnoi S., Department of Marketing, Institute of Management Studies Ghaziabad, Ghaziabad, India; Agarwal V., Department of Marketing, Institute of Management Studies Ghaziabad, Ghaziabad, India
Description
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Purpose: The online learning environment is a function of dynamic market forces constantly restructuring the e-learning landscapes complete ecosystemcape. This study aims to propose an e-learning framework by integrating the Technology Acceptance Model (TAM) and Theory of Planned Behaviour (TPB) to predict students Online Learning Readiness and Behaviour. Design/methodology/approach: A structured questionnaire was used to collect data from 406 students through a survey. The data were analysed using two-stage structural equation modelling and artificial neural network (ANN). Findings: The studys results revealed that perceived ubiquity (PUB) positively influences perceived ease of use, usefulness and attitude. Similarly, perceived mobility significantly influences perceived ease of use and attitude. Furthermore, attitude, subjective norms, perceived behavioural control and perceived usefulness significantly influence readiness to learn online, which further influences students online learning behaviour. The root-mean-square error (RMSE) values obtained from the ANN analysis indicate the models predictive solid accuracy. Originality/value: The study contributes to the existing literature by proposing an Online Learning Behaviour Model by integrating the TAM and the TPB frameworks in association with two additional constructs, PUB and Perceived Mobility. Secondly, this study proposes a unique triangulation framework of recommendations for learners, educators and policymakers. 2024, Emerald Publishing Limited.
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Artificial neural networks (ANN); Online learning behaviour; Perceived mobility; Perceived ubiquity; Readiness to learn online; Technology acceptance model (TAM); Theory of planned behaviour
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Emerald Publishing
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ISSN: 2046469X;
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English
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Article
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online
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Faculty Publications
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Gupta, Srikant; Jha, Babita; Dawar, Gaurav
Title
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An Integrated Pythagorean Fuzzy Delphi-AHP Framework for Optimizing Foreign Direct Investment: Key Drivers for Success
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01-01-2025
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Annals of Data Science;
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<a href="https://doi.org/10.1007/s40745-025-00667-z" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s40745-025-00667-z</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105025963891?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105025963891?origin=resultslist</a>
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Gupta S., Department of Operations and Decision Sciences, Jaipuria Institute of Management, Jaipur, India; Jha B., School of Business and Management, Christ (Deemed to Be University), NCR, Ghaziabad, India; Dawar G., Jaypee Business School, Jaypee Institute of Information Technology, Uttar Pradesh, Noida, India
Description
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Foreign Direct Investment (FDI) plays a pivotal role in global economic development, fostering cross-border collaborations and driving economic growth. Recognizing the significance of optimizing FDI drivers, this study employs a novel approach by integrating the Pythagorean Fuzzy Delphi (PFD) and Pythagorean Fuzzy Analytic Hierarchy Process (PFAHP). The Pythagorean Fuzzy Delphi methodology was used to identify and classify drivers into Technological, Political, Environmental, Social, and Cultural categories. Subsequently, the PFAHP was employed to rank these drivers. The top three prioritized drivers are: advocating for favorable foreign investment policies and trade agreements; implementing advanced cybersecurity measures to safeguard sensitive technology and data; and developing cutting-edge research and development facilities to foster innovation and attract technology-intensive investments. The study concludes by discussing how implementing these top-ranked drivers can significantly enhance FDI by creating a conducive environment for international investment, thereby contributing to economic prosperity and technological advancement. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.
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Drivers; Foreign direct investment; Pythagorean fuzzy AHP; Pythagorean fuzzy delphi
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Springer Science and Business Media Deutschland GmbH
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ISSN: 21985804;
Language
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English
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Article
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Format
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online
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Articles
Article
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An Integrated Reinforcement DQNN Algorithm to Detect Crime Anomaly Objects in Smart Cities
Subject
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Advanced Encryption Standard (AES); anomaly detection; crime rate prediction; HybridFly; RCNN; reinforcement; security attacks
Description
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In olden days it is difficult to identify the unsusceptible forces happening in the society but with the advancement of smart devices, government has started constructing smart cities with the help of IoT devices, to capture the susceptible events happening in and around the surroundings to reduce the crime rate. But, unfortunately hackers or criminals are accessing these devices to protect themselves by remotely stopping these devices. So, the society need strong security environment, this can be achieved with the usage of reinforcement algorithms, which can detect the anomaly activities. The main reason for choosing the reinforcement algorithms is it efficiently handles a sequence of decisions based on the input captured from the videos. In the proposed system, the major objective is defined as minimum identification time from each frame by defining if then decision rules. It is a sort of autonomous system, where the system tries to learn from the penalties posed on it during the training phase. The proposed system has obtained an accuracy of 98.34% and the time to encrypt the attributes is also less. 2021. All Rights Reserved.
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Mandala D.J.; Akhila P.; Reddy V.S.
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International Journal of Advanced Computer Science and Applications, Vol-12, No. 12, pp. 348-352.
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Science and Information Organization
Date
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2021-01-01
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<a href="https://doi.org/10.14569/IJACSA.2021.0121246" target="_blank" rel="noreferrer noopener">https://doi.org/10.14569/IJACSA.2021.0121246</a>
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All Open Access; Gold Open Access
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ISSN: 2158107X
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Online
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English
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Article
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Mandala D.J., Department of CSE, School of Engineering & Technology, CHRIST (Deemed to be University), Bengaluru, India; Akhila P., Department of CSE, Gayatri Vidya Parishad College of Engineering (A), Andhra Pradesh, Visakhapatnam, India; Reddy V.S., Department of IT, VBIT Telangana, India
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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An Integrated Scalable Healthcare Management System Using IOT
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And Sensors; Android; ECC; Mlab; Raspberry Pi; SHMS
Description
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Healthcare management is the challenging task of maintaining the patients medical-related data and images. Pervasive computing, which consists of a wireless network, is an innovative medium for medical data transmission. Here, we propose SHMS (Scalable Healthcare Management System) and interoperability, an available and user-friendly platform. It utilizes a huge amount of data and medical images that must be managed and stored for processing and further investigation. In our work, data like heartbeat, temperature, blood pressure, and ECG readings are collected using different sensors and in one gateway protocol. This design is used for transferring, managing, and accessing documents containing health-related information, which is scattered across different system and organization domains. It is scalable because cloud platforms provide communication APIs, the web service interfaces ensure interoperability, the availability makes patients, doctors, or administrators able to access medical-related data anywhere, and Android OS makes it user-friendly. The security of the data collected can be achieved by authenticating storage using a cryptographic ECC algorithm. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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Jalapur S.; Yogish D.; Mithun B.N.; Anoop G.L.
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Lecture Notes in Networks and Systems, Vol-856 LNNS, pp. 165-178.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-981-97-7571-2_14" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-7571-2_14</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214396557&doi=10.1007%2F978-981-97-7571-2_14&partnerID=40&md5=783ebb8102a3fc2f113d6b9dd11e4365" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214396557&doi=10.1007%2f978-981-97-7571-2_14&partnerID=40&md5=783ebb8102a3fc2f113d6b9dd11e4365</a>
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ISSN: 23673370; ISBN: 978-981977548-4
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Online
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English
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Conference paper
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Jalapur S., CSE Department, Christ University, Bangalore, India; Yogish D., CSE Department, Christ University, Bangalore, India; Mithun B.N., CSE (Cyber Security) Department, A P S College of Engineering, Bangalore, India; Anoop G.L., CSE Department, Christ University, Bangalore, India
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Articles
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An Integrated Segmentation Techniques for Myocardial Ischemia
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fuzzy C-means; magnetic resonance imaging; median filtering; morphological operations; myocardial infarction; segmentation
Description
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Abstract: Myocardial Ischemia segmentation is a challenging task for basic and translational research on cardiovascular, as it provides ultimately realistic in heart muscle model. The main objective of the research work is to find an efficient segmentation technique for the myocardial ischemia based on the myocardial infarcted MRI data set for the accurate classification of scar volume. The paper will give an insight about the segmentation technique based on myocardial ischemia and discusses essential cellular components. The paper provides an integrated approach which comprises of fuzzy c-means and morphological operations along with median filtering enhancement technique help in detecting the myocardial ischemia. The developed model is tested with 2D and 3D enhanced myocardial ischemia MRI and also with normal heart. The purpose of segmentation in myocardial ischemia is to identify the scar region in the heart. The integrated model is evaluated based on statistical measures and validated based on manual segmentation done by clinical expert. The scar classification is done based on the myocardial ischemia segmentation which leads to better prediction of arrhythmia in heart patient. The integrated model is considered as one of the best model for segmenting myocardial ischemia. 2020, Pleiades Publishing, Ltd.
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Merjulah R.; Chandra J.
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Pattern Recognition and Image Analysis, Vol-30, No. 3, pp. 530-540.
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Pleiades Publishing
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2020-01-01
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<a href="https://doi.org/10.1134/S1054661820030190" target="_blank" rel="noreferrer noopener">https://doi.org/10.1134/S1054661820030190</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091002285&doi=10.1134%2FS1054661820030190&partnerID=40&md5=290dfadd595e096e0c4f5a47193a41e9" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091002285&doi=10.1134%2fS1054661820030190&partnerID=40&md5=290dfadd595e096e0c4f5a47193a41e9</a>
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ISSN: 10546618
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Online
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English
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Article
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Merjulah R., CHRIST (Deemed to be University), Hosur Road, Bangalore, 560029, Karnataka, India; Chandra J., CHRIST (Deemed to be University), Hosur Road, Bangalore, 560029, Karnataka, India
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Conference Papers
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Faculty Publications- Conference Papers
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An Integration of AI Technique in the Field of Healthcare Industry
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AI; Biological Computation; Biomedicine; Disease Diagnoses; Healthcare; Living Support; Neural Networks; Predictive Studies; Research; Scientific Successes; Technological Environment
Description
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Over the last few years, the field of intelligent machines (AI) has experienced fast improvements in software algorithms to hardware deployment, and varied uses, especially in the area of healthcare. This thorough study aims to capture recent developments in AI uses within biomedicine, spanning disease diagnoses, living support, biological computation, and research. The primary goal is to record recent scientific successes, discern what is happening in the technological environment, perceive the enormous future scope of AI on biomedicine along and serve as a source of stimulus for researchers through related fields. It is obvious that, similar to the development of AI itself, the use of it in biology continues to remain in its infant state. This review expects ongoing breakthroughs and improvements that will push the limits and broaden the range of AI uses in the near future. In order to communicate the changing possibility of AI in biology, the study dives into individual case studies. These include anticipating of epileptic seizure events and the uses of AI in treating a faulty urine bladder. By studying these cases, the overview seeks to explain the visible impact of AI off healthcare and reinforce the chance of immediate developments in this evolving and promising field. 2024 IEEE.
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Priyadharsini P.; Khare N.; Praveenraj D.D.W.; Senthurya S.; Arunkumar B.; Reddy G.V.
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2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 1981-1986.
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Date
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2024-01-01
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<a href="https://doi.org/10.1109/ICACITE60783.2024.10617363" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICACITE60783.2024.10617363</a>
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ISBN: 979-835036016-5
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Online
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English
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Conference paper
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Priyadharsini P., Nehru Institute of Engineering and Technology, T.M.Palayam, Coimbatore, India; Khare N., Ies University, Ies Insititute of Technology and Management, M.P., Bhopal, 462044, India; Praveenraj D.D.W., Christ University, Bangalore, India; Senthurya S., Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India; Arunkumar B., Karpagam Academy of Higher Education, Coimbatore, India; Reddy G.V., Cmr Technical Campus, Hyderabad, Medchal, India
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Conference Papers
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Faculty Publications- Conference Papers
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Title
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An integration of big data and cloud computing
Subject
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Big data; Cloud computing; Integration; Research; Software
Description
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In this era, Big data and Cloud computing are the most important topics for organizations across the globe amongst the plethora of softwares. Big data is the most rapidly expanding research tool in understanding and solving complex problems in different interdisciplinary fields such as engineering, management health care, e-commerce, social network marketing finance and others. Cloud computing is a virtual service which is used for computation, data storage, data mining by creating flexibility and at minimum cost. It is pay & use model which is the next generation platform to analyse the various data which comes along with different services and applications without physically acquiring them. In this paper, we try to understand and work on the integration model of both Cloud Computing and Big Data to achieve efficiency and faster outcome. It is a qualitative paper to determine the synergy. Springer Science+Business Media Singapore 2017.
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Thingom C.; Yeon G.
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Advances in Intelligent Systems and Computing, Vol-469, pp. 729-737.
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Springer Verlag
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2017-01-01
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<a href="https://doi.org/10.1007/978-981-10-1678-3_70" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-10-1678-3_70</a>
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ISSN: 21945357; ISBN: 978-981101677-6
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Online
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English
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Conference paper
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Thingom C., Centre for Digital Innovation, Christ University, Bangalore, India; Yeon G., Centre for Digital Innovation, Christ University, Bangalore, India
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Conference Papers
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Title
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An Integration of Satellite A Based Network with Higher Level Type Network with the use of P-P Connection: A Deep Review
Subject
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6G; Aerial Access Network (AAN); Benders decomposition-based algorithm; point to point communication; Time-Expanding graphs (TEGs)
Description
An account of the resource
The Aerial Access 6g Network (AAN) is seen as a way to access remote and sparsely populated areas not served by traditional terrestrial networks, especially with the advent of 6G technology. This study presents a new approach for efficient data collection and transmission in point to point access networks using low earth orbit (LEO) satellites and high altitude platforms (HAPS). Incorporating LEO satellites as backlinks and HAPs as airborne base stations, the system provides low-bandwidth transmission to ground users. A Time Augmented Graph (TEG) model is proposed to represent the dynamic topology of the air access network according to time slots. With this example, this study can create an entire programming problem with the goal of maximizing data transfer to the country's data processing centre (DPC) while respecting resource constraints. Benders' decomposition-based algorithm (BDA) is proposed to solve the NP-hardness of the problem and is shown to perform well in producing near-optimal solutions. The effectiveness and efficiency of the proposed strategy is verified through simulation results performed in a realistic environment, showing high speed and performance comparable to search methods. By informing the design and optimization of future communication systems, this study will provide a better understanding of how HAP and LEO satellites work together in aerial access networks for the collection and delivery of remote terrain data. 2024 IEEE.
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Kumar V.N.; Al-Attabi K.; Thakur G.; Ambrose S.; Nirsanametla Y.; Murugajothi T.
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2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024, pp. 1392-1396.
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Institute of Electrical and Electronics Engineers Inc.
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2024-01-01
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<a href="https://doi.org/10.1109/ICACITE60783.2024.10617062" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICACITE60783.2024.10617062</a>
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ISBN: 979-835036016-5
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Online
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English
Type
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Conference paper
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Kumar V.N., Cmr Technical Campus, Department of Cse, Telangana, Hyderabad, India; Al-Attabi K., The Islamic University, Najaf, Iraq; Thakur G., Uttaranchal Institute of Technology, Uttaranchal University, Department of Civil Engineering, Dehradun, 248007, India; Ambrose S., Christ (Deemed to Be University), Department of Commerce, India; Nirsanametla Y., Chitkara University, Centre of Research Impact and Outreach, Punjab, Rajpura, 140401, India; Murugajothi T., Psna College of Engineering and Technology, Department of Ece, Dindigul, India
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Faculty Publications
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Singh, Davinder Paul; Rani, Rozy; Sindhu, V.; Revathi, V.M.; Kosalairaman, T.; Paul, P. Mano
Title
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An Intelligent Approach for Breast Cancer Diagnosis Using Fuzzy Logic and Extreme Learning Machine
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01-01-2025
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2025 Global Conference on Information Technology and Communication Networks, GITCON 2025;
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<a href="https://doi.org/10.1109/GITCON65266.2025.11377847" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/GITCON65266.2025.11377847</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035604073?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035604073?origin=resultslist</a>
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Singh D.P., Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gujarat, Gandhinagar, India; Rani R., Applied Sciences, Chandigarh Group of Colleges Jhanjeri, Chandigarh Engineering College, Punjab, Mohali, India; Sindhu V., Department of Computer Science, Christ University, Karnataka, Bangalore, India; Revathi V.M., Department of Mathematics, Bannari Amman Institute of Technology, Sathyamangalam, Erode, India; Kosalairaman T., Department of Information Technology, V. S. B. Engineering College, Tamilnadu, Karur, India; Paul P.M., Department of CSE, Artificial Intelligence, Dayananda Sagar Academy of Technology and Management, Karnataka, Udayapura, India
Description
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The long-term prognosis and mortality rates can be improved with early identification of breast cancer. The time-consuming and expensive procedures of mammography, MRI, ultrasound, CT, PT, and biopsy have been the subject of much research; nevertheless, these approaches are not suitable for younger women and can be rather expensive. This study employed cutting-edge image processing to improve early breast cancer detection. The researchers utilised anisotropic filtering to reduce background noise in medical images after picking mammograms at random from the Digital Database for Screening Mammography. The use of morphology-based feature extraction allowed for autonomous and accurate categorisation after mass segmentation using a genetic algorithm with recurrent thresholding. By merging a KF with an ELM enhanced with an AV, a new model named KF-av-elm improves diagnostic accuracy. Medical imaging noise and estimating errors are both significantly reduced by the combination method. Their accuracy rating of 98.28% allowed them to outperform other approaches. The KF-av-elm model appears to be a reliable, efficient, and effective diagnostic tool; its adoption may lead to better identification and outcomes for breast cancer patients. 2025 IEEE.
Subject
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African Vultures Optimization Algorithm (AVOA); Breast Cancer (BC); Deoxyribonucleic Acid (DNA); Genetic Algorithm (GA); Kalman filter (KF)
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833153348-9;
Language
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English
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Conference paper
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Faculty Publications
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Ambrose, Adebanjo Falade; Agarwal, Gaurav; Sanghi, Akash; Panwar, Sarika; Upreti, Kamal; Jain, Rituraj
Title
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An intelligent black-box testing model for isolating logical flaws and anomalies in applications using GTMRM
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01-01-2025
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International Journal of Information Technology (Singapore);
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<a href="https://doi.org/10.1007/s41870-025-02871-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s41870-025-02871-9</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022654162?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022654162?origin=resultslist</a>
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Ambrose A.F., Department of Computer Science Engineering, Invertis University, Bareilly, India; Agarwal G., Department of Computer Science Engineering, Invertis University, Bareilly, India; Sanghi A., Department of Computer Applications, Invertis University, Bareilly, India; Panwar S., Department of Electronics and Telecommunication Engineering, MIT Academy of Engineering, Pune, India; Upreti K., Department of Computer Science, Christ University, Delhi NCR Campus, Ghaziabad, India; Jain R., Department of Information Technology, Marwadi University, Gujarat, Rajkot, India
Description
An account of the resource
Web Applications (WAs) are becoming more vulnerable to attacks as they are more popular. Nevertheless, the conventional testing methodologies didnt differentiate the Logical Flaws (LFs) and anomalies in WAs, thereby increasing the misclassification rate. Hence, in this paper, a novel black-box testing framework that incorporates an advanced technique called Gated Transformer Memorized transferred Recurrent Mishswish unit (GTMRM) is proposed for distinguishing between LFs and other vulnerabilities, thus enhancing the reliability of WAs. Initially, the user registration is carried out, followed by Hash-based Message Authentication Code Hash-based Message Authentication Code (HMAC) creation. Afterward, the registered users log into the application to request a Uniform Resource Locator (URL) for access. In the meantime, to authenticate the user, the HMAC verification is performed. Once the authentication is successful, the user is granted for accessing the functionalities. Thereafter, the black-box-centric LF and anomaly identification is done; here, the raw dataset is initially pre-processed. Subsequently, concerning a similar domain, the pre-processed data is clustered. Next, the features are extracted, followed by feature selection. Then, from the grouped data, the graph is constructed. The pattern labelling is carried out centered on the graph features. Lastly, the Logical Flaws (LF), anomaly, and legitimate access are proficiently classified by the proposed GTMRM. A compensation measure is applied in the case of a LF. After that, the data is securely stored in the cloud server with an accuracy of 99.14%. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.
Subject
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Automated black-box testing; Data privacy; Exponent belled fuzzy inference system; Logical flaws; Web security
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Springer Science and Business Media B.V.
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ISSN: 25112104;
Language
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English
Type
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Article
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Restricted Access; Hardcopy may be available in the library
Format
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online
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Title
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Conference Papers
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An Intelligent Business Automation with Conversational Web Based Build Operate Transfer (BOT)
Subject
The topic of the resource
Artificial Intelligent; Deep Neural Network; Natural Language Processing; the Build operate transfer; Voice Recognition
Description
An account of the resource
The field of AI chatbots with voice help capabilities has seen significant advancements recently because to the usage of NLP (Natural Language Processing), NLG (Natural Language Generation), and (DNN) Deep Neural Networks. Using the expanding skills of chatbots, which are assisted by AI and ML technologies, a variety of business challenges may be handled. Profitability is one of the most crucial features of a business. This is only achievable if top-level management is aware of the company's costs, revenues, and human resource performance. In this case, an AI-powered chatbot with voice help may be utilised to evaluate corporate data and provide a report. The Bot knows the meaning of words and responds to them thanks to the wordnet in the corpus. Corpus is basically a dictionary for ChatBot. Top management may ask the Bot anything, and the Bot will quickly undertake exploratory data analysis and create a report. The Bot first understands the data using feature selection and then performs exploratory data analysis. After the EDA technique, Bot activates the voice recognition mode to understand the question and give answers. The Bot can use a male or female voice (depending on the developer). Then BOT provides a data table and visualisations for better understanding. 2020 Copyright for this paper by its authors.
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Bajaj A.; Preethi N.
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CEUR Workshop Proceedings, Vol-3283, pp. 300-306.
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CEUR-WS
Date
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2021-01-01
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<a href="" target="_blank" rel="noreferrer noopener"></a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143199543&partnerID=40&md5=6b36e75bd6d1bc77fd47662ef697812f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143199543&partnerID=40&md5=6b36e75bd6d1bc77fd47662ef697812f</a>
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Restricted Access
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ISSN: 16130073
Format
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Online
Language
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English
Type
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Conference paper
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Bajaj A., CHRIST (Deemed to be University), 30, Valor Ct, Maharashtra, Lavasa, 412112, India; Preethi N., CHRIST (Deemed to be University), 30, Valor Ct, Maharashtra, Lavasa, 412112, India
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Faculty Publications
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Faculty Publications- Conference Papers
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Joshi, Satvik; Sharma, Harshvardhan; Singh, Yajush Pratap; Ahmed, Sayed Abu Lais Ezaz; Sharma, Vandana
Title
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An Intelligent Cognitive Framework for Crime Prediction in Smart Cities using Video Mining
Date
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01-01-2025
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2025 International Conference on Artificial Intelligence and Machine Vision, AIMV 2025;
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<a href="https://doi.org/10.1109/AIMV66517.2025.11203581" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AIMV66517.2025.11203581</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105021819749?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105021819749?origin=resultslist</a>
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Joshi S., Kalinga Institute of Industrial Technology, School of Computer Engineering, Bhubaneswar, India; Sharma H., Kalinga Institute of Industrial Technology, School of Computer Engineering, Bhubaneswar, India; Singh Y.P., IIT Patna, Department of Computer Science and Engineering, India; Ahmed S.A.L.E., Chandigarh University, Department of Computer Science and Engineering, Punjab, India; Sharma V., CHRIST University, Computer Science Department, Bengaluru, India
Description
An account of the resource
Booming development in cities with dense population have led to urban policing and public safety emerging as urgent concerns in city environments.current monitoring practices including CCTV'S and other IOT sensors generate a vast amount of data ,thus making them inadequate for the task. However a combination of video mining,computer vision,artificial intelligence and data mining techniques,do offer us a better framework for monitoring and real-time detection of crime in Smart city"s environment. This paper proposes an intelligent and Cognitive framework for prediction of crime. by combining various advanced modals such as YOLO (You took Only Look Once) for detecting objects, 3D Convolutional Neural Networks (CNN) for recognizing actions, deep SORT for tracking multiple objects, One-class SVM for detecting anomaly and LSTM for behavioral analysis. These modals can organized to function in a coherent system which can be organized to distinguish examine and trail illegal activities such of mugging, robbery, pick pocketing, violence, utilizing available live video feeds. By efficient date processing, and overcoming shortcomings such of limited labeled datasets and real-time feed detection, this framework can provide practical conclusion making tool for law enforcement in urban smart city environments which can enhance urban safety.Besides effective crime detection,this tool compiles with established ethical standards such as upholding privacy and legal compliance. 2025 IEEE.
Subject
The topic of the resource
Action recognition; Anomaly detection; Artificial intelligence (AI); Computer vision; Crime prediction; Deep learning; Legal compliance; Object detection; Privacy; Smart cities; Video mining
Publisher
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833152697-9;
Language
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English
Type
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Conference paper
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Restricted Access; Hardcopy may be available in the library
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Articles
Article
Faculty Publications -Articles
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An Intelligent Decision Support System to Aid Profit Planning in Manufacturing Companies
Subject
The topic of the resource
Break-Even Analysis; Management Accounting; Market Demand; Multi-product Break Even Point; Profit Planning
Description
An account of the resource
In order to assure accuracy in profit planning and decision-making, this study uses an intelligent decision support system to investigate an appropriate approach for calculating the "Break-Even" point in multi-product segments while taking into account the implications for contribution margin, demand, and capacity. The research's methodology and findings may be used to propose new projects, grow businesses, and make decisions in processes that focus on many products. Data are used to illustrate the advanced level of break-even analysis and application, and a description of the convenient and system-generated method of computation is given. A mathematical approach has been used based on actual data to show how to determine the break-even point without sacrificing the influencing aspects such as contribution margin, capacity, product mix, and demand for each. The researchers have created a good system application-oriented platform to make it simple to calculate the break-even point, which will be crucial for decision-making and profit planning even with more than 500 SKU (Stock Keeping Unit). This research evaluated the data and created formulas for actual data structure-based analysis. The study's conclusions have a significant influence on those companies that need to determine the true break-even threshold. The challenge area of concern might be the applicability of this activity for other sectors and other countries as this research was centred on the plastic bag industry in Malaysia. Future research can also analyse other important factors like start-up and semi-variable costs as they are not included in the current study. The identified break-even threshold can still be used effectively given the current market demand and the product's capacity. 2023, Ismail Saritas. All rights reserved.
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Thasan S.; Rahman A.; Subramanian P.; Williams M.J.
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International Journal of Intelligent Systems and Applications in Engineering, Vol-11, No. 4s, pp. 345-356.
Publisher
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Ismail Saritas
Date
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2023-01-01
Identifier
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<a href="" target="_blank" rel="noreferrer noopener"></a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161368879&partnerID=40&md5=c8d2479715084f14df3cb0363b46ab0e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161368879&partnerID=40&md5=c8d2479715084f14df3cb0363b46ab0e</a>
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Restricted Access
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A related resource
ISSN: 21476799
Format
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Online
Language
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English
Type
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Article
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Thasan S., Faculty Of Business and Accounting, Linton University College, Malaysia; Rahman A., Faculty of Business and Communications, INTI International University, Malaysia; Subramanian P., School of Business Management, Christ University, Lavasa, Pune, India; Williams M.J., Sri Krishna Arts and Science College, India
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
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Jose, Sebastian; Hiremath, Manjunatha; Pohrmen, Fabiola
Title
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An Intelligent Framework for Evaluating Handwritten Responses: Integrating Bloom's Taxonomy with Adaptive Assessment
Date
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01-01-2025
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Proceedings of the 2025 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2025;pp.1385-1389
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<a href="https://doi.org/10.1109/AISummit66170.2025.11411255" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AISummit66170.2025.11411255</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035326213?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035326213?origin=resultslist</a>
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Jose S., Christ Deemed to be University, Bengaluru, India; Hiremath M., Christ Deemed to be University, Bengaluru, India; Pohrmen F., Christ Deemed to be University, Bengaluru, India
Description
An account of the resource
Traditional manual grading of descriptive-type answer scripts is inefficient and laborious, while existing technologies rely on strict keyword matching and cosine similarity, which fail to capture the semantic meaning and argumentative quality. This paper proposes a multi-layered intelligent framework for evaluating handwritten descriptive answer scripts by integrating Revised Bloom's Taxonomy with adaptive assessment methods. The system consists of a multi-dimensional evaluation strategy comprising four valuation metrics, namely, content relevance, coherence, depth, and argumentation quality. When compared with conventional methods of keyword matching or computing the cosine similarity, this proposed framework evaluates the semantic meaning and argumentative structure while adapting to varying response styles and contextual differences for personalised assessment. 2025 IEEE.
Subject
The topic of the resource
Argument Mining; Coherence; Content Relevance; Depth; Google Cloud Vision; Natural Language Processing; Revised Bloom's Taxonomy; Semantic Analysis; Transformers
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Relation
A related resource
ISBN: 979-833155379-1;
Language
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English
Type
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Conference paper
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Restricted Access; Hardcopy may be available in the library
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Articles
Article
Faculty Publications -Articles
Dublin Core
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Title
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An Intelligent Hybrid GA-PI Feature Selection Technique for Network Intrusion Detection Systems
Subject
The topic of the resource
Feature selection; Hybrid GA-PI algorithm; Network Intrusion Detection Systems (NIDS); Support vector machines (SVM)
Description
An account of the resource
The development of Network Intrusion Detection Systems (NIDS) has become increasingly important due to the growing threat of cyber-attacks. However, with the vast amount of data generated in networks, handling big data in NIDS has become a major challenge. To address this challenge, this research paper proposes an intelligent hybrid GA-PI algorithm for feature selection and classification tasks in NIDS using support vector machines (SVM). The proposed approach is evaluated using two sub-datasets, Analysis and Normal, and Reconnaissance and Normal, which are generated from the publicly available UNSWNB-15 dataset. In this work, instead of considering all possible attacks, the focus is on two attacks, emphasizing the importance of the feature selection agent in determining the optimal features based on the attack type. The experimental results show that the proposed hybrid feature selection approach outperforms existing methodologies in terms of accuracy and execution time. Moreover, the selection of features can be subjective and dependent on the domain knowledge of the researcher. Additionally, the proposed approach requires computational resources for feature selection and classification tasks, which can be a limitation for resource-constrained systems. To be brief, this research paper presents a promising approach for feature selection and classification tasks in NIDS using an intelligent hybrid GA-PI algorithm. While there are some challenges and limitations, the proposed approach has the potential to contribute to the development of effective and efficient NIDS. 2023, Ismail Saritas. All rights reserved.
Creator
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Sowmya; Mary Anita T.
Source
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International Journal of Intelligent Systems and Applications in Engineering, Vol-11, No. 7s, pp. 718-731.
Publisher
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Ismail Saritas
Date
A point or period of time associated with an event in the lifecycle of the resource
2023-01-01
Identifier
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<a href="" target="_blank" rel="noreferrer noopener"></a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165153935&partnerID=40&md5=61ac7ed13d9a5cb1b861cf350fb54da6" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165153935&partnerID=40&md5=61ac7ed13d9a5cb1b861cf350fb54da6</a>
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Restricted Access
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A related resource
ISSN: 21476799
Format
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Online
Language
A language of the resource
English
Type
The nature or genre of the resource
Article
Coverage
The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant
Sowmya, Christ University & Assistant professor, CMRIT, Karnatka, Bangalore, India; Mary Anita T., Christ University Bangalore, Karnatka, Bangalore, India
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Articles
Article
Faculty Publications -Articles
Dublin Core
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Title
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An intelligent inventive system for personalised webpage recommendation based on ontology semantics
Subject
The topic of the resource
Ontologies; Personalised; Semantic strategy; Web search; Webpage recommendation system
Description
An account of the resource
Owing to the information diversity in the web and its dynamically changing contents, extraction of relevant information from the web is a huge challenge. With the World Wide Web transforming into a more organised semantic web, the incorporation of semantic techniques to retrieve relevant information is highly necessary. In this paper, a dynamic ontology alignment technique for recommending relevant webpages is proposed. The strategy focuses on knowledge tree construction by computing the semantic similarity between the query terms as well as the ontological entities. Furthermore, the semantic similarity is again computed between nodes of the constructed knowledge tree and URLs in the URL repository to recommend relevant webpages. The dynamic ontology alignment by computing their respective semantic similarity constitutes Ontology Semantics. Personalisation is achieved by prioritisation of webpages by content-based analysis of the users web usage data. An overall accuracy of 87.73% is achieved by the proposed approach. Copyright 2019 Inderscience Enterprises Ltd.
Creator
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Deepak G.; Ahmed A.; Skanda B.
Source
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International Journal of Intelligent Systems Technologies and Applications, Vol-18, No. 45689, pp. 115-132.
Publisher
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Inderscience Publishers
Date
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2019-01-01
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<a href="https://doi.org/10.1504/IJISTA.2019.097751" target="_blank" rel="noreferrer noopener">https://doi.org/10.1504/IJISTA.2019.097751</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061298512&doi=10.1504%2FIJISTA.2019.097751&partnerID=40&md5=ed644d47c5eb3c9bc994e25cf6d17c67" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061298512&doi=10.1504%2fIJISTA.2019.097751&partnerID=40&md5=ed644d47c5eb3c9bc994e25cf6d17c67</a>
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All Open Access; Bronze Open Access
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ISSN: 17408865
Format
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Online
Language
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English
Type
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Article
Coverage
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Deepak G., Department of Computer Science and Engineering, Faculty of Engineering, Christ University, Bangalore, 560074, India; Ahmed A., RSA Business Unit, Dell EMC, Bangalore, 560048, India; Skanda B., Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore, 560001, India
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Adhisekar, Kamalakkannan; Mulla, Gulnar Sadat; Thirumalaikumari, T.; Vanitha, P.; Kamepalli, Sujatha; Acharjee, Purnendu Bikash
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An Intelligent Method for Fraud Detection in Digital Payments based on SVR with GC-RF Approach
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01-01-2025
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2025 International Conference on Intelligent Computing and Knowledge Extraction, ICICKE 2025;
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<a href="https://doi.org/10.1109/ICICKE65317.2025.11136358" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICICKE65317.2025.11136358</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022297396?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022297396?origin=resultslist</a>
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Adhisekar K., Department of Corporate Secretaryship & Accounting and Finance, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, India; Mulla G.S., University of Technology Bahrain Salmabad, Bahrain; Thirumalaikumari T., Department of Computer Science, Saveetha College of Liberal Arts and Sciences, Saveetha Institute of Medical and Technical Science, Chennai, India; Vanitha P., Department of Management Studies, M. Kumarasamy College of Engineering, Karur, India; Kamepalli S., Department of Information Technology, Vignan's Foundation for Science Technology and Research, Guntur, India; Acharjee P.B., Department of Computer Science, CHRIST University, Bangalore, India
Description
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The use of automated algorithms to detect fraud on electronic payment networks is challenging. Digital payment systems and their users are vulnerable to cybercriminals who take advantage of security holes or users' negligence to steal passwords, perpetrate fraud, launder money, and carry out other malicious acts. Conventional methods of fraud detection are challenging to execute because of the difficulties of acquiring massive volumes of manually annotated data. It is tough to notice new trends because fraudsters are often changing their techniques. Feature extraction, model training, and data preprocessing were the main areas of emphasis in this systematic research. Data pretreatment encompassed tasks such as acquiring training sample data, cleaning, converting, integrating, and altering the data. Feature extraction is the backbone of SVR-GC-RF model training; it takes all the data in a dataset and turns it into features. The suggested method outperformed SVR and RF in terms of accuracy by 95.23 percent. The importance of hierarchical fraud detection in online payment systems is highlighted in this paper. Through the use of effective feature extraction and model training, the study enhances fraud detection. Methods for detecting fraud need to change if they are to keep up with the criminals. 2025 IEEE.
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digital transformation; fraud detection (FD); grey catastrophe point (GC); support vector regression
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833153681-7;
Language
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English
Type
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Conference paper
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Restricted Access; Hardcopy may be available in the library
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Faculty Publications
Conference Paper
Faculty Publications- Conference Papers
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Moorthy, C.; Shafeek, A.; Paul, Eldho; Madhan, S.; Eswar, D.; Naveenkumar, R.
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An Intelligent Model for Detecting Cervical Cancer Using U-Net Segmentation and YOLO Classification Augmented with Lion Optimization
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01-01-2025
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Proceedings of the 9th International Conference on Inventive Systems and Control, ICISC 2025;pp.715-722
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<a href="https://doi.org/10.1109/ICISC65841.2025.11188165" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICISC65841.2025.11188165</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020936146?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020936146?origin=resultslist</a>
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Moorthy C., Dr. Mahalingam College of Engineering and Technology, Electronics and Communication Engineering, Coimbatore, Pollachi, India; Shafeek A., Dr. Mahalingam College of Engineering and Technology, Electronics and Communication Engineering, Coimbatore, Pollachi, India; Paul E., Electronics and Communication Engineering Christ(Deemed to Be University), Bangalore, India; Madhan S., Dr. Mahalingam College of Engineering and Technology, Electronics and Communication Engineering, Coimbatore, Pollachi, India; Eswar D., Dr. Mahalingam College of Engineering and Technology, Electronics and Communication Engineering, Coimbatore, Pollachi, India; Naveenkumar R., Dr. Mahalingam College of Engineering and Technology, Electronics and Communication Engineering, Coimbatore, Pollachi, India
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One of the leading causes of death for women is cervical cancer, and survival rates are significantly increased by early identification using Pap smear analysis. However, manually reviewing Pap smear images takes time and is prone to mistakes. Using an enhanced Convolutional Neural Network (CNN) with Transformer-like classification and U-Net-based nucleus segmen-tation, this study suggests an automated cervical cancer detection system. To increase feature extraction, the image is preprocessed using techniques such as edge detection, contrast enhancement with CLAHE, and greyscale conversion. The PR-processed image is segmented using U Net segmentation. A YoLo-based CNN optimised with the Lion optimiser (Evolved Sign Momentum) is used to classify the segmented nuclei to improve convergence and accuracy. Results from experiments show that our model outperforms con-ventional methods in terms of classification accuracy. By lowering reliance on manual screening and increasing early diagnosis rates, this automated approach can help medical practitioners detect cervical cancer more quickly and accurately. 2025 IEEE.
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Cervical Cancer; CNN; Deep Learning; Lion Optimizer; Medical Image Segmentation; Pap Smear; U-Net
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Institute of Electrical and Electronics Engineers Inc.
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ISBN: 979-833151247-7;
Language
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English
Type
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Conference paper
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Restricted Access; Hardcopy may be available in the library
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
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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/.
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An Intelligent Model forPost Covid Hearing Loss
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covid-19; mathematical model; ordinary differential equations; stability; Sudden sensorineural hearing loss (SSHL)
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Several viral infections tend to cause Sudden Sensorineural Hearing Loss (SSNHL) in humans. Covid-19 being a viral disease could also cause hearing deficiencies in people as a side effect. There have been pieces of evidence from various case studies wherein covid infected patients have reported to be suffering from sudden sensorineural hearing loss. The main objective of this study is to inspect the phenomenon and treatment of SSNHL in post-COVID-19 patients. This study proposes a mathematical model of hearing loss as a consequence of covid-19 infection using ordinary differential equations. The solutions obtained for the model are established to be non-negative and bounded. The disease-free equilibrium, endemic equilibrium and basic reproductive number have been obtained for the model which helps analyse the models trend through stability analysis. Moreover, numerical simulations have been performedfor validating the obtained theoretical results. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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Poonia R.C.; Aarthi S.; Samanta D.
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Lecture Notes in Networks and Systems, Vol-1046 LNNS, pp. 39-46.
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Springer Science and Business Media Deutschland GmbH
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2024-01-01
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<a href="https://doi.org/10.1007/978-3-031-64813-7_5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-64813-7_5</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200691556&doi=10.1007%2F978-3-031-64813-7_5&partnerID=40&md5=59952371375d5ae850f4206198d533f2" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200691556&doi=10.1007%2f978-3-031-64813-7_5&partnerID=40&md5=59952371375d5ae850f4206198d533f2</a>
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ISSN: 23673370; ISBN: 978-303164812-0
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Online
Language
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English
Type
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Conference paper
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Poonia R.C., CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Aarthi S., CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Samanta D., Department of Computing and Information Technologies, RIT Kosovo (A.U.K), Rochester Institute of Technology - RIT Global Campus, Prishtina, Kosovo, 10000
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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/.
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Conference Papers
Conference Paper
Faculty Publications- Conference Papers
Dublin Core
The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/.
Title
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An Intelligent Portfolio Management Scheme Based On Hybrid Deep Reinforcement Learning and Cumulative Prospective Approach
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Assets; Deep reinforcement learning; Optimal Return; Optimization; Portfolio selection; Stock price prediction
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Stock markets retain an extensive role towards economic growth of diverse countries and it is a place where investors invest assured amount to earn more profit and the issuers pursue the investors for project investing. However, it is deliberated as a challenging task to buy and sell because of its explosive and complex nature. The existing portfolio optimization models are primarily focused on just improving the returns whereas, the selection of optimal assets is least focused. Hence, the proposed research article focuses on the integration of stock prediction with the portfolio optimization model (SPPO). Initially, the stock prices for the next period are predicted using the hybrid deep reinforcement learning (DRL) model. Within this prediction model, the gated recurrent unit network (GRUN) model is utilized to simulate the interactions of the agent with the environment. The best actions in the prediction model are determined throughout the prediction process using the quantum differential evolution algorithm (Q-DEA). After the prediction of best assets, the optimal portfolio with the best assets is selected using the cumulative prospect theory (CPT) model. The work will be implemented in python and evaluated using the NIFTY-50 Stock Market Data (2000 -2021) dataset. Minimal error rates of 0.130, 0.114, 0.148 and 0.153 is obtained by the proposed model in case of MSE, MAE, RMSE and MAPE. 2024 IEEE.
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Ramaiah K.; Soundarabai P.B.
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Proceedings of ICWITE 2024: IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, pp. 117-123.
Publisher
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Institute of Electrical and Electronics Engineers Inc.
Date
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2024-01-01
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<a href="https://doi.org/10.1109/ICWITE59797.2024.10503521" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICWITE59797.2024.10503521</a>
<br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192562600&doi=10.1109%2FICWITE59797.2024.10503521&partnerID=40&md5=4955509a44cd3910fb93bea3a7ac2ab9" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192562600&doi=10.1109%2fICWITE59797.2024.10503521&partnerID=40&md5=4955509a44cd3910fb93bea3a7ac2ab9</a>
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Restricted Access
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ISBN: 979-835038328-7
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Online
Language
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English
Type
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Conference paper
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Ramaiah K., Christ (Deemed to Be University), Department of Computer Science, Bengaluru, 560029, India; Soundarabai P.B., Christ (Deemed to Be University), Department of Computer Science, Bengaluru, 560029, India