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Archive Issue – Vol.3, Issue.2 (Apr-Jun 2023)

Archive Issue – Vol.3, Issue.2 (April-June 2023)


Exploring Modern Systems for Campus Placement Management: A Comprehensive Review

Aditi Desai, Adrija Singh, Harsh Soni, Usha Gupta, Divya Khushwah

Research Paper | Journal Paper

Vol.3, Issue.2, pp.1-05, June-2023

Abstract References Citation Download

Abstract

The Training and Placement cell at most colleges currently use WhatsApp to communicate a lot of its operations. Although this method of communication is effective, it may not be efficient due to the presence of several groups and messages, some students may overlook a few notices and critical information. The project's main objective is to centralize TnP activities, notifications, job listings, and Resume submissions to streamline and improve the placement process and increase candidates' chances of success.

Key-Words / Index Term: Placement, Management system, TnP, TPO, Campus.

References

    • J. Nicholine, P. Lakshmi, S. Ilakkiya, M. Kartheeswari, and D. Kethrin, "Placement Management System," International Journal of Progressive Research in Engineering Management and Science (IJPREMS), vol. 3, no. 4, pp. 120–124, Apr. 2023. doi: 10.58257/IJPREMS3084
    • S. Chaurasia, "Student Internship Placement Management System using Python," International Journal of Research in Science & Engineering, May 2023. doi: 10.55529/ijrise.33.30.49
    • T. Panchal, M. Wadke, and A. Sedamkar, "Placement Management System," International Research Journal of Engineering and Technology (IRJET), vol. 9, no. 4, p. 2584, Apr. 2022. [Online].
    • A. Banu and M. S. K. Bargavi, "A Research on Placement Management System," International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 5, Apr. 2022. [Online].
    • S. Padwal, S. Ghorpade, P. R. Patil, M. Patil, S. Biraje, and S. Salunkhe, "E-Training and Placement Management System," International Research Journal of Modernization in Engineering Technology and Science, vol. 4, no. 6, p. 4324, Jun. 2022. [Online].
    • G. Dhopavkar, T. Kale, R. Gaikwad, S. Chavan, G. Kumar, H. Shendare, and H. Akre, "An Integrated Web Application for Training and Placement," Publication Date: Aug. 19, 2022. [Online].
    • P. Teli, A. Anpat, T. Thorat, and M. Mulla, "A Web Based ERP for Placement Management," International Research Journal of Modernization in Engineering Technology and Science, vol. 4, no. 12, p. 1515, Dec. 2022. doi: 10.56726/IRJMETS32537
    • F. Rizvi, N. A. Khan, S. Upadhyay, S. Suryawanshi, and S. Pappu, "Placement Management System," Journal of Emerging Technologies and Innovative Research (JETIR), vol. 8, no. 4, Apr. 2021. [Online].
    • M. Sayyed, F. Umatiya, S. Zehera, and S. Pappu, "College Placement Management System," International Journal of Creative Research Thoughts (IJCRT), vol. 8, no. 6, p. 2020, Jun. 2020. ISSN: 2320-2882. [Online].
    • A. Sunny, A. Felix, A. Saji, C. Sebastian, and P. V. M., "Placement Management System for Campus Recruitment," International Journal of Innovative Science and Research Technology, vol. 5, no. 5, May 2020. ISSN: 2456-2165. [Online].

Citation

Aditi Desai, Adrija Singh, Harsh Soni, Usha Gupta, Divya Khushwah "Exploring Modern Systems for Campus Placement Management: A Comprehensive Review" International Journal of Scientific Research in Technology & Management, Vol.3, Issue.2, pp.1-05, 2023.

Examining the Influence of Customer Relationship Management on Customer Satisfaction in Indian Private Banks

Shyamasundar Tripathy

Research Paper | Journal Paper

Vol.3, Issue.1, pp.06-11, Jun-2023

Abstract References Citation Download

Abstract

This study examines the pivotal role of Customer Relationship Management (CRM) in shaping customer satisfaction within the context of Indian private banks. Customer satisfaction is a crucial factor in the success of any banking institution, and CRM strategies play a vital role in fostering positive relationships with customers. By conducting a comprehensive analysis of Indian private banks, this research aims to shed light on the impact of CRM practices on customer satisfaction levels. The findings of this study are expected to provide valuable insights for bank managers and policymakers to enhance their CRM approaches and ultimately improve overall customer satisfaction in the Indian banking sector.

Key-Words / Index Term: Customer Relationship Management, Customer Satisfaction, Private Banking Sector, Performance of Banks.

References

  1. Das, K., Parmar, J., & Sadanand, V. K. (2009). Customer relationship management (CRM) best practices and customer loyalty: A study of Indian retail banking sector. European Journal of Social Sciences, 11(1), 61–85.
  2. Das, S. K. (2012). Customer relationship management in banking sector: A comparative study of SBI and other nationalised commercial banks in India. Arth Prabandh: A Journal of Economics and Management, 1(6), 2278–2629.
  3. Gayathry, S. (2016). Customer relationship management model for banks. Journal of Internet Banking and Commerce, 21(S5), 1.
  4. Gopalsamy, S., & Gokulapadmanaban, S. (2021). Does implementation of customer relationship management (CRM) enhance customer loyalty? An empirical research in banking sector. Iranian Journal of Management Studies, 14(2), 401–417. doi: 10.22059/ijms.2020.303861.674181
  5. Gupta, M. P., & Shukla, S. (2002). Learnings from customer relationship management (CRM) implementation in a bank. Global Business Review, 3(1), 99–122. doi: 10.1177/097215090200300107
  6. Iriqat, R. A., & Daqar, A. (2017). The role of customer relationship management on enhancing customers’ satisfaction in banks in Palestine. Modern Applied Science, 11(12), 84–91. doi: 10.5539/mas.v11n12p84
  7. Kumar, P., Mokha, A. K., & Pattnaik, S. C. (2021). Electronic customer relationship management (E-CRM), customer experience and customer satisfaction: Evidence from the banking industry. Benchmarking: An International Journal. doi: 10.1108/BIJ-04-2020-0181
  8. Mohammed, M. F. (2013). Customer relationship management (telecommunication industry): Comparison between Airtel and Zain. International Journal of Business and Management Invention, 2(11), 52–58.
  9. Padmavathy, C., Balaji, M. S., & Sivakumar, V. J. (2012). Measuring effectiveness of customer relationship management in Indian retail banks. International Journal of Bank Marketing, 30(4), 246–266. doi: 10.1108/02652321211236888
  10. Popli, G. S., & Rao, D. N. (2009). Customer relationship management in Indian banks. SSRN Electronic Journal. doi: 10.2139/ssrn.1373592
  11. Shakeel, M., Barsaiyan, S., & Sijoria, C. (2020). Twitter as a customer service management platform: A study on Indian banks. Journal of Content, Community & Communication, 11(6), 84–104. doi: 10.31620/JCCC.06.20/07

Citation

Shyamasundar Tripathy, "Examining the Influence of Customer Relationship Management on Customer Satisfaction in Indian Private Banks" International Journal of Scientific Research in Technology & Management, Vol.3, Issue.2, pp.06-11, 2023.

Dual Purpose Autonomous Self Learning based Training Device for People with Alzheimer and Visually Challenged

J. Hemavathy, A. Akshaya, P.K. Narmadha, K. Nithya

Research Paper | Journal Paper

Vol.3, Issue.2, pp.12-19, Jun-2023

Abstract References Citation Download

Abstract

Alzheimer’s disease is a progressive brain disorder, irreversible that slowly destroys memory and thinking skills. Recent studies have demonstrated that potential of visual interventions to improve the functioning of AD patients. Therefore, clarification of visual deficits in AD and possible mechanisms underlying these deficits are needed. We Propose a camera-based detection for visually impaired or Alzheimer person to identify the texts on the printed labels or books, names of the objects in real time, and names of the known person using face detection and Sign languages. To read the texts from books and printed documents an OCR (Optical Character Recognition) method is used to convert the Image to Texts. Once the images converted to text. Using Machine Learning it is ease to detect the known faces, real time objects and text to audio. The Predicted output will be Convert to Audio using Text to Speech API. The Main objective is to design a tool which supports multiple disabilities and provides solution efficiently. This Concept is designed to suit people with Dementia (Alzheimer) and for people who are visually impaired.

Key-Words / Index Term: Automatic image extraction, Artificial Intelligence, Optical Character Recognition, Tesseract, Face detection, object detection.

References

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  7. Joseph, J., & Zacharia, K. P. (2013). Automatic attendance management system using face recognition. International Journal of Science and Research (IJSR).
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  12. Petrov, Y. (2017). Improving object detection by exploiting semantic relations between objects (Master’s thesis). Universitat Politècnica de Catalunya.
  13. Martínez, R., Cuevas, C., Berjón, D., & García, N. (2015). Detection of static moving objects using multiple nonparametric background models (pp. 4–5).
  14. Gupta, M. R., Jacobson, N. P., & Garcia, E. K. OCR binarization and image preprocessing for searching historical documents.
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Citation

J. Hemavathy, A. Akshaya, P.K. Narmadha, K. Nithya "Dual Purpose Autonomous Self Learning based Training Device for People with Alzheimer and Visually Challenged" International Journal of Scientific Research in Technology & Management, Vol.3, Issue.2, pp.12-19, 2023.

A Review on Automatic Liver Cancer Detection in Digital Image Processing

Vijay Laxmi, Anubhuti Khare

Research Paper | Journal Paper

Vol.3, Issue.2, pp.20-26, Jun-2023

Abstract References Citation Download

Abstract

Liver cancer is one of the most important health related problems in the world. Grading diagnosis for liver cancer in liver cancer treatment requires biopsy images diagnosis. Artificial grading system for extracting knowledge to give quantitative and objective results for the physicians and pathologists; it not only saves time but also improving the accuracy of the diagnosis. However, inappropriate vision and complex stroma background affects partition performance. In this paper, a review has been conducted for better analysis of liver cancer diagnosis system. Liver cancer is usually diagnosed by three different tests: blood test, image test and biopsy. To make the task of diagnosing liver cancer simpler and less time consuming, an effective approach is to be adopted. This research puts forward a computer-aided diagnostic system to diagnose liver cancer. Some detection method that researcher uses MRI, CT and USG scan imaging along with various feature extraction method.

Key-Words / Index Term: Liver Cancer Detection, Support Vector Machine, CT Scans, Segmentation, MRI, Edge Detection.

References

  1. Cancer Homoeo Clinic. (2020, July 15). Liver cancer. https://cancerhomoeoclinic.co.in/diseases/liver-cancer/
  2. WebMD. (2020, July 15). Understanding liver cancer—the basics. https://www.webmd.com/cancer/understanding-liver-cancer-basic-information#1
  3. Romero, F. P., et al. (2019). End-to-end discriminative deep network for liver lesion classification. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy. IEEE.
  4. Dutta, A., & Dubey, A. (2019). Detection of liver cancer using image processing techniques. 2019 International Conference on Communication and Signal Processing (ICCSP), Chennai, India. IEEE.
  5. Priya, L., Saraswathi, D., & Lakshmi, R. P. (2019). Liver segmentation using weighted contrast-based Chan–Vese method. 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), Pondicherry, India. IEEE.
  6. Jabarulla, M. Y., & Lee, H. (2018). Evaluating the effect of various speckle reduction filters on ultrasound liver cancer images. 2018 International Conference on Electronics, Information, and Communication (ICEIC), Honolulu, HI. IEEE.
  7. Sabut, S., Das, A., Acharya, U. R., & Panda, S. (2018). Deep learning-based liver cancer detection using watershed transform and Gaussian mixture model techniques. Cognitive Systems Research. https://doi.org/10.1016/j.cogsys.2018.12.009
  8. Azer, S. A. (2019). Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review. World Journal of Gastrointestinal Oncology, 11(12), 1218–1230.
  9. Rajesh, G., & Priyadharson, S. M. (2018). Liver cancer detection and classification based on optimum hierarchical feature fusion with PeSOA and PNN classifier. Biomedical Research, 29(1).
  10. Ghuse, N., Deore, Y., & Potgantwar, A. (2017). Efficient image processing-based liver cancer detection method. International Journal of Scientific Research in Network Security and Communication, 5(3), 33–38.
  11. Upadhyay, Y., & Wasson, V. (2014). Analysis of liver MR images for cancer detection using genetic algorithm. International Journal of Engineering Research and General Science, 2(4).
  12. Zhen, S., et al. (2020). Deep learning for accurate diagnosis of liver tumor based on magnetic resonance imaging and clinical data. Frontiers in Oncology, 10, 680.
  13. Zhou, H., & Yang, C. (2018). Flexible Nano-Ag@paper biosensor and its application in detection of liver cancer. 2018 IEEE 13th Annual International Conference on Nano/Micro Engineered and Molecular Systems (NEMS).
  14. Khan, M. W., Tai, S., & Lo, Y. (2018). Using deep learning to evaluate the segmentation of liver cell from biopsy image. 2018 9th International Conference on Awareness Science and Technology (iCAST), Fukuoka. IEEE.
  15. Reddy, D. S., Bharath, R., & Rajalakshmi, P. (2018). A novel computer-aided diagnosis framework using deep learning for classification of fatty liver disease in ultrasound imaging. 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom), Ostrava. IEEE. https://doi.org/10.1109/HealthCom.2018.8531118
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  17. Anisha, P. R., Reddy, C. K. K., & Prasad, L. V. N. (2015). A pragmatic approach for detecting liver cancer using image processing and data mining techniques. International Conference on Signal Processing and Communication Engineering Systems (SPACES), India.
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Citation

Vijay Laxmi, Anubhuti Khare "A Review on Automatic Liver Cancer Detection in Digital Image Processing" International Journal of Scientific Research in Technology & Management, Vol.3, Issue.2, pp.20-26, 2023.